Lyubov sobol

Comment

Author: Admin | 2025-04-28

Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay . 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research Vol. 12 (2011), 2825--2830.[32]M. Reif, F. Shafait, and A. Dengel . 2012. Meta-learning for evolutionary parameter optimization of classifiers. Machine learning Vol. 87, 3 (2012), 357--380.[33]D. W. Scott . 2015. Multivariate density estimation: theory, practice, and visualization. John Wiley & Sons.[34]J. Snoek, H. Larochelle, and R. P. Adams . 2012. Practical Bayesian optimization of machine learning algorithms Advances in neural information processing systems 25. ACM, 2951--2959.[35]C. Soares, P. Brazdil, and P. Kuba . 2004. A meta-learning method to select the kernel width in support vector regression. Machine learning Vol. 54, 3 (2004), 195--209.[36]I. M. Sobol . 1993. Sensitivity estimates for nonlinear mathematical models. Mathematical Modelling and Computational Experiments Vol. 1, 4 (1993), 407--414.[37]J. T. Springenberg, A. Klein, S. Falkner, and F. Hutter . 2016. Bayesian optimization with robust Bayesian neural networks Advances in Neural Information Processing Systems 29, bibfieldeditorD. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett (Eds.). Curran Associates, Inc., 4134--4142.[38]K. Swersky, J. Snoek, and R. Adams . 2013. Multi-task Bayesian optimization. In Advances in Neural Information Processing Systems 26. 2004--2012.[39]C. Thornton, F. Hutter, H. Hoos, and K. Leyton-Brown . 2013. Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. In Proc. of ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD). 847--855.[40]J. N. van Rijn . 2016. Massively Collaborative Machine Learning. Ph.D. Dissertation. bibinfoschoolLeiden University.[41]J. N. van Rijn, S. M. Abdulrahman, P. Brazdil, and J. Vanschoren . 2015. Fast Algorithm Selection using Learning Curves. In Advances in Intelligent Data Analysis XIV. Springer, 298--309.[42]J. N. van Rijn and F. Hutter . 2017. An Empirical Study of Hyperparameter Importance Across Datasets Proc. of AutoML 2017 @ ECML-PKDD. CEUR-WS, 97--104.[43]J. Vanschoren, J. N. van Rijn, B. Bischl, and L. Torgo . 2014. OpenML: networked science in machine learning. ACM SIGKDD Explorations Newsletter Vol. 15, 2 (2014), 49--60.[44]M. Wistuba, N. Schilling, and L. Schmidt-Thieme . 2015. Hyperparameter search space pruning--a new component for sequential model-based hyperparameter optimization. In Proc. of ECML/PKDD 2015. Springer, 104--119.Information & ContributorsInformationPublished In KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningJuly 20182925 pagesCopyright © 2018 ACM.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].PublisherAssociation for Computing MachineryNew York, NY, United StatesPublication HistoryPublished: 19 July 2018PermissionsRequest permissions for this article.Check for updatesAuthor Tagshyperparameter importancehyperparameter optimizationmeta-learningQualifiersResearch-articleFunding

Add Comment