0519370 - ÚI 2020 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
Pešková, K. - Neruda, Roman
Hyperparameters search methods for machine learning linear workflows.
18th IEEE International Conference on Machine Learning and Applications ICMLA 2019. Proceedings. Piscataway: IEEE, 2019, s. 1205-1210. ISBN 978-1-7281-4550-1.
[ICMLA 2019: IEEE International Conference on Machine Learning and Applications /18./. Boca Raton (US), 16.12.2019-19.12.2019]
Grant CEP: GA ČR(CZ) GA18-23827S
Institucionální podpora: RVO:67985807
Klíčová slova: hyperparameters optimization * machine learning workflows * data preprocessing
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Hyperparameters optimization is one of the most important metalearning features that is used in AutoML systems. In this paper we use hyperparameters-space search algorithms to optimize the settings of supervised machine learning methods and workflows. We focus on changes in performance of hyperparameters optimization algorithms with the growing complexity of the hyperparameters-space, when using the data preprocessings adds more parameters to the configuration and thus more dimensions to the searched space.
Trvalý link: http://hdl.handle.net/11104/0304362