Below is a list of publications that I was involved in.\nFeel free to contact me if you have any questions, feedback or comments about the presented research, preferably via email:<\/p>\n
martin.zaefferer (at) gmx.de<\/p>\n
Bartz-Beielstein, Thomas; Chandrasekaran, Sowmya; Rehbach, Frederik; Zaefferer, Martin<\/p>
Case Study I: Tuning Random Forest (Ranger)<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 187\u2013220, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023b,\r\ntitle = {Case Study I: Tuning Random Forest (Ranger)},\r\nauthor = {Thomas Bartz-Beielstein and Sowmya Chandrasekaran and Frederik Rehbach and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_8},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {187\u2013220},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_8<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zaefferer, Martin; Chandrasekaran, Sowmya<\/p>Case Study IV: Tuned Reinforcement Learning (in Python)<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 271\u2013281, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023a,\r\ntitle = {Case Study IV: Tuned Reinforcement Learning (in Python)},\r\nauthor = {Martin Zaefferer and Sowmya Chandrasekaran},\r\ndoi = {10.1007\/978-981-19-5170-1_11},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {271\u2013281},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_11<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zaefferer, Martin; Mersmann, Olaf; Bartz-Beielstein, Thomas<\/p>Global Study: Influence of Tuning<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 283\u2013301, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023,\r\ntitle = {Global Study: Influence of Tuning},\r\nauthor = {Martin Zaefferer and Olaf Mersmann and Thomas Bartz-Beielstein},\r\ndoi = {10.1007\/978-981-19-5170-1_12},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {283\u2013301},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_12<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Hyperparameter Tuning Approaches<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 71\u2013119, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 187\u2013220, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023b,\r\ntitle = {Case Study I: Tuning Random Forest (Ranger)},\r\nauthor = {Thomas Bartz-Beielstein and Sowmya Chandrasekaran and Frederik Rehbach and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_8},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {187\u2013220},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_8<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zaefferer, Martin; Chandrasekaran, Sowmya<\/p>Case Study IV: Tuned Reinforcement Learning (in Python)<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 271\u2013281, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023a,\r\ntitle = {Case Study IV: Tuned Reinforcement Learning (in Python)},\r\nauthor = {Martin Zaefferer and Sowmya Chandrasekaran},\r\ndoi = {10.1007\/978-981-19-5170-1_11},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {271\u2013281},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_11<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zaefferer, Martin; Mersmann, Olaf; Bartz-Beielstein, Thomas<\/p>Global Study: Influence of Tuning<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 283\u2013301, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023,\r\ntitle = {Global Study: Influence of Tuning},\r\nauthor = {Martin Zaefferer and Olaf Mersmann and Thomas Bartz-Beielstein},\r\ndoi = {10.1007\/978-981-19-5170-1_12},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {283\u2013301},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_12<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Hyperparameter Tuning Approaches<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 71\u2013119, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023b,\r\ntitle = {Case Study I: Tuning Random Forest (Ranger)},\r\nauthor = {Thomas Bartz-Beielstein and Sowmya Chandrasekaran and Frederik Rehbach and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_8},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {187\u2013220},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_8<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zaefferer, Martin; Chandrasekaran, Sowmya<\/p>Case Study IV: Tuned Reinforcement Learning (in Python)<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 271\u2013281, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023a,\r\ntitle = {Case Study IV: Tuned Reinforcement Learning (in Python)},\r\nauthor = {Martin Zaefferer and Sowmya Chandrasekaran},\r\ndoi = {10.1007\/978-981-19-5170-1_11},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {271\u2013281},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_11<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zaefferer, Martin; Mersmann, Olaf; Bartz-Beielstein, Thomas<\/p>Global Study: Influence of Tuning<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 283\u2013301, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023,\r\ntitle = {Global Study: Influence of Tuning},\r\nauthor = {Martin Zaefferer and Olaf Mersmann and Thomas Bartz-Beielstein},\r\ndoi = {10.1007\/978-981-19-5170-1_12},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {283\u2013301},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_12<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Hyperparameter Tuning Approaches<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 71\u2013119, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
@incollection{BartzBeielstein2023b,\r\ntitle = {Case Study I: Tuning Random Forest (Ranger)},\r\nauthor = {Thomas Bartz-Beielstein and Sowmya Chandrasekaran and Frederik Rehbach and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_8},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {187\u2013220},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_8<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zaefferer, Martin; Chandrasekaran, Sowmya<\/p>Case Study IV: Tuned Reinforcement Learning (in Python)<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 271\u2013281, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023a,\r\ntitle = {Case Study IV: Tuned Reinforcement Learning (in Python)},\r\nauthor = {Martin Zaefferer and Sowmya Chandrasekaran},\r\ndoi = {10.1007\/978-981-19-5170-1_11},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {271\u2013281},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_11<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zaefferer, Martin; Mersmann, Olaf; Bartz-Beielstein, Thomas<\/p>Global Study: Influence of Tuning<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 283\u2013301, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023,\r\ntitle = {Global Study: Influence of Tuning},\r\nauthor = {Martin Zaefferer and Olaf Mersmann and Thomas Bartz-Beielstein},\r\ndoi = {10.1007\/978-981-19-5170-1_12},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {283\u2013301},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_12<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Hyperparameter Tuning Approaches<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 71\u2013119, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_8<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zaefferer, Martin; Chandrasekaran, Sowmya<\/p>Case Study IV: Tuned Reinforcement Learning (in Python)<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 271\u2013281, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023a,\r\ntitle = {Case Study IV: Tuned Reinforcement Learning (in Python)},\r\nauthor = {Martin Zaefferer and Sowmya Chandrasekaran},\r\ndoi = {10.1007\/978-981-19-5170-1_11},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {271\u2013281},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_11<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zaefferer, Martin; Mersmann, Olaf; Bartz-Beielstein, Thomas<\/p>Global Study: Influence of Tuning<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 283\u2013301, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023,\r\ntitle = {Global Study: Influence of Tuning},\r\nauthor = {Martin Zaefferer and Olaf Mersmann and Thomas Bartz-Beielstein},\r\ndoi = {10.1007\/978-981-19-5170-1_12},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {283\u2013301},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_12<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Hyperparameter Tuning Approaches<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 71\u2013119, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Close<\/a><\/p><\/div><\/div><\/div> Zaefferer, Martin; Chandrasekaran, Sowmya<\/p>Case Study IV: Tuned Reinforcement Learning (in Python)<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 271\u2013281, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023a,\r\ntitle = {Case Study IV: Tuned Reinforcement Learning (in Python)},\r\nauthor = {Martin Zaefferer and Sowmya Chandrasekaran},\r\ndoi = {10.1007\/978-981-19-5170-1_11},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {271\u2013281},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_11<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zaefferer, Martin; Mersmann, Olaf; Bartz-Beielstein, Thomas<\/p>Global Study: Influence of Tuning<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 283\u2013301, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023,\r\ntitle = {Global Study: Influence of Tuning},\r\nauthor = {Martin Zaefferer and Olaf Mersmann and Thomas Bartz-Beielstein},\r\ndoi = {10.1007\/978-981-19-5170-1_12},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {283\u2013301},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_12<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Hyperparameter Tuning Approaches<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 71\u2013119, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Zaefferer, Martin; Chandrasekaran, Sowmya<\/p>
Case Study IV: Tuned Reinforcement Learning (in Python)<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 271\u2013281, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023a,\r\ntitle = {Case Study IV: Tuned Reinforcement Learning (in Python)},\r\nauthor = {Martin Zaefferer and Sowmya Chandrasekaran},\r\ndoi = {10.1007\/978-981-19-5170-1_11},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {271\u2013281},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_11<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zaefferer, Martin; Mersmann, Olaf; Bartz-Beielstein, Thomas<\/p>Global Study: Influence of Tuning<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 283\u2013301, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023,\r\ntitle = {Global Study: Influence of Tuning},\r\nauthor = {Martin Zaefferer and Olaf Mersmann and Thomas Bartz-Beielstein},\r\ndoi = {10.1007\/978-981-19-5170-1_12},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {283\u2013301},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_12<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Hyperparameter Tuning Approaches<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 71\u2013119, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 271\u2013281, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023a,\r\ntitle = {Case Study IV: Tuned Reinforcement Learning (in Python)},\r\nauthor = {Martin Zaefferer and Sowmya Chandrasekaran},\r\ndoi = {10.1007\/978-981-19-5170-1_11},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {271\u2013281},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_11<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zaefferer, Martin; Mersmann, Olaf; Bartz-Beielstein, Thomas<\/p>Global Study: Influence of Tuning<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 283\u2013301, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023,\r\ntitle = {Global Study: Influence of Tuning},\r\nauthor = {Martin Zaefferer and Olaf Mersmann and Thomas Bartz-Beielstein},\r\ndoi = {10.1007\/978-981-19-5170-1_12},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {283\u2013301},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_12<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Hyperparameter Tuning Approaches<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 71\u2013119, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023a,\r\ntitle = {Case Study IV: Tuned Reinforcement Learning (in Python)},\r\nauthor = {Martin Zaefferer and Sowmya Chandrasekaran},\r\ndoi = {10.1007\/978-981-19-5170-1_11},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {271\u2013281},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_11<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zaefferer, Martin; Mersmann, Olaf; Bartz-Beielstein, Thomas<\/p>Global Study: Influence of Tuning<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 283\u2013301, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023,\r\ntitle = {Global Study: Influence of Tuning},\r\nauthor = {Martin Zaefferer and Olaf Mersmann and Thomas Bartz-Beielstein},\r\ndoi = {10.1007\/978-981-19-5170-1_12},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {283\u2013301},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_12<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Hyperparameter Tuning Approaches<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 71\u2013119, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
@incollection{Zaefferer2023a,\r\ntitle = {Case Study IV: Tuned Reinforcement Learning (in Python)},\r\nauthor = {Martin Zaefferer and Sowmya Chandrasekaran},\r\ndoi = {10.1007\/978-981-19-5170-1_11},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {271\u2013281},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_11<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zaefferer, Martin; Mersmann, Olaf; Bartz-Beielstein, Thomas<\/p>Global Study: Influence of Tuning<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 283\u2013301, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023,\r\ntitle = {Global Study: Influence of Tuning},\r\nauthor = {Martin Zaefferer and Olaf Mersmann and Thomas Bartz-Beielstein},\r\ndoi = {10.1007\/978-981-19-5170-1_12},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {283\u2013301},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_12<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Hyperparameter Tuning Approaches<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 71\u2013119, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_11<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zaefferer, Martin; Mersmann, Olaf; Bartz-Beielstein, Thomas<\/p>Global Study: Influence of Tuning<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 283\u2013301, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023,\r\ntitle = {Global Study: Influence of Tuning},\r\nauthor = {Martin Zaefferer and Olaf Mersmann and Thomas Bartz-Beielstein},\r\ndoi = {10.1007\/978-981-19-5170-1_12},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {283\u2013301},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_12<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Hyperparameter Tuning Approaches<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 71\u2013119, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Close<\/a><\/p><\/div><\/div><\/div> Zaefferer, Martin; Mersmann, Olaf; Bartz-Beielstein, Thomas<\/p>Global Study: Influence of Tuning<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 283\u2013301, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023,\r\ntitle = {Global Study: Influence of Tuning},\r\nauthor = {Martin Zaefferer and Olaf Mersmann and Thomas Bartz-Beielstein},\r\ndoi = {10.1007\/978-981-19-5170-1_12},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {283\u2013301},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_12<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Hyperparameter Tuning Approaches<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 71\u2013119, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Zaefferer, Martin; Mersmann, Olaf; Bartz-Beielstein, Thomas<\/p>
Global Study: Influence of Tuning<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 283\u2013301, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023,\r\ntitle = {Global Study: Influence of Tuning},\r\nauthor = {Martin Zaefferer and Olaf Mersmann and Thomas Bartz-Beielstein},\r\ndoi = {10.1007\/978-981-19-5170-1_12},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {283\u2013301},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_12<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Hyperparameter Tuning Approaches<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 71\u2013119, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 283\u2013301, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023,\r\ntitle = {Global Study: Influence of Tuning},\r\nauthor = {Martin Zaefferer and Olaf Mersmann and Thomas Bartz-Beielstein},\r\ndoi = {10.1007\/978-981-19-5170-1_12},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {283\u2013301},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_12<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Hyperparameter Tuning Approaches<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 71\u2013119, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{Zaefferer2023,\r\ntitle = {Global Study: Influence of Tuning},\r\nauthor = {Martin Zaefferer and Olaf Mersmann and Thomas Bartz-Beielstein},\r\ndoi = {10.1007\/978-981-19-5170-1_12},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {283\u2013301},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_12<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Hyperparameter Tuning Approaches<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 71\u2013119, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
@incollection{Zaefferer2023,\r\ntitle = {Global Study: Influence of Tuning},\r\nauthor = {Martin Zaefferer and Olaf Mersmann and Thomas Bartz-Beielstein},\r\ndoi = {10.1007\/978-981-19-5170-1_12},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {283\u2013301},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_12<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Hyperparameter Tuning Approaches<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 71\u2013119, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_12<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Hyperparameter Tuning Approaches<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 71\u2013119, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Hyperparameter Tuning Approaches<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 71\u2013119, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>
Hyperparameter Tuning Approaches<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 71\u2013119, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 71\u2013119, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
@incollection{BartzBeielstein2023a,\r\ntitle = {Hyperparameter Tuning Approaches},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_4},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {71\u2013119},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_4<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Close<\/a><\/p><\/div><\/div><\/div> Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Ed.)<\/p>
Hyperparameter Tuning for Machine and Deep Learning with R<\/a> Book<\/span> <\/p>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
@book{Bartz2023,\r\ntitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\neditor = {Eva Bartz and Thomas Bartz-Beielstein and Martin Zaefferer and Olaf Mersmann},\r\ndoi = {10.1007\/978-981-19-5170-1},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {book}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin<\/p>Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Models<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 27\u201369, <\/span>Springer, <\/span>2023<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
@incollection{BartzBeielstein2023c,\r\ntitle = {Models},\r\nauthor = {Thomas Bartz-Beielstein and Martin Zaefferer},\r\ndoi = {10.1007\/978-981-19-5170-1_3},\r\nyear = {2023},\r\ndate = {2023-01-01},\r\nbooktitle = {Hyperparameter Tuning for Machine and Deep Learning with R},\r\npages = {27\u201369},\r\npublisher = {Springer},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {incollection}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Close<\/a><\/p><\/div><\/i>doi:10.1007\/978-981-19-5170-1_3<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Close<\/a><\/p><\/div><\/div><\/div> Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Hellstern, Gerhard; Dehn, Vanessa; Zaefferer, Martin<\/p>
Quantum computer based Feature Selection in Machine Learning<\/a> Journal Article<\/span> <\/p>In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
In: <\/span>2023<\/span>.<\/p>Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
@article{Hellstern2023,\r\ntitle = {Quantum computer based Feature Selection in Machine Learning},\r\nauthor = {Gerhard Hellstern and Vanessa Dehn and Martin Zaefferer},\r\ndoi = {10.48550\/ARXIV.2306.10591},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\npublisher = {arXiv},\r\nabstract = {The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Close<\/a><\/p><\/div>The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.<\/div>Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Close<\/a><\/p><\/div><\/i>doi:10.48550\/ARXIV.2306.10591<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Close<\/a><\/p><\/div><\/div><\/div> Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Zhang, Yingqian; Bliek, Laurens; Costa, Paulo; Afshar, Reza Refaei; Reijnen, Robbert; Catshoek, Tom; Vos, Dani\u00ebl; Verwer, Sicco; Schmitt-Ulms, Fynn; Hottung, Andr\u00e9; Shah, Tapan; Sellmann, Meinolf; Tierney, Kevin; Perreault-Lafleur, Carl; Leboeuf, Caroline; Bobbio, Federico; Pepin, Justine; Silva, Warley Almeida; Gama, Ricardo; Fernandes, Hugo L.; Zaefferer, Martin; L\u00f3pez-Ib\u00e1\u00f1ez, Manuel; Irurozki, Ekhine<\/p>
The first AI4TSP competition: Learning to solve stochastic routing problems<\/a> Journal Article<\/span> <\/p>In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
In: <\/span>Artificial Intelligence, <\/span>vol. 319, <\/span>pp. 103918, <\/span>2023<\/span>, ISSN: 0004-3702<\/span>.<\/p>Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
@article{Zhang2023,\r\ntitle = {The first AI4TSP competition: Learning to solve stochastic routing problems},\r\nauthor = {Yingqian Zhang and Laurens Bliek and Paulo Costa and Reza Refaei Afshar and Robbert Reijnen and Tom Catshoek and Dani\u00ebl Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr\u00e9 Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L\u00f3pez-Ib\u00e1\u00f1ez and Ekhine Irurozki},\r\ndoi = {10.1016\/j.artint.2023.103918},\r\nissn = {0004-3702},\r\nyear = {2023},\r\ndate = {2023-06-01},\r\njournal = {Artificial Intelligence},\r\nvolume = {319},\r\npages = {103918},\r\npublisher = {Elsevier BV},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Close<\/a><\/p><\/div><\/i>doi:10.1016\/j.artint.2023.103918<\/a><\/li><\/ul><\/div>Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Close<\/a><\/p><\/div><\/div><\/div> Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf<\/p>
Tuning: Methodology<\/a> Book Section<\/span> <\/p>In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>
In: <\/span>Hyperparameter Tuning for Machine and Deep Learning with R, <\/span>pp. 7\u201326, <\/span>Springer, <\/span>2023<\/span>.<\/p>