Adaptive Selection of Gaussian Process Model for Active Learning in Expensive Optimization

0493292 - ÚI 2019 RIV IE eng A - Abstrakt
Repický, Jakub - Pitra, Zbyněk - Holeňa, Martin
Adaptive Selection of Gaussian Process Model for Active Learning in Expensive Optimization.
ECML PKDD 2018: Workshop on Interactive Adaptive Learning. Proceedings. Dublin, 2018 - (Krempl, G.; Lemaire, V.; Kottke, D.; Calma, A.; Holzinger, A.; Polikar, R.; Sick, B.). s. 80-84
[ECML PKDD 2018: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. 10.09.2018-14.09.2018, Dublin]
Grant CEP: GA ČR GA17-01251S
Institucionální podpora: RVO:67985807
Klíčová slova: Gaussian process * Surrogate model * Black-box optimization * Active Learning
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://www.ies.uni-kassel.de/p/ial2018/ialatecml2018.pdf

Trvalý link: http://hdl.handle.net/11104/0286678