0494112 - ÚI 2019 RIV DE eng C - Conference Paper (international conference)
Pitra, Zbyněk - Repický, Jakub - Holeňa, MartinBoosted Regression Forest for the Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy.
ITAT 2018: Information Technologies – Applications and Theory. Proceedings of the 18th conference ITAT 2018. Aachen: Technical University & CreateSpace Independent Publishing Platform, 2018 - (Krajči, S.), s. 72-79. CEUR Workshop Proceedings, V-2203. ISSN 1613-0073.
[ITAT 2018. Conference on Information Technologies – Applications and Theory /18./. Plejsy (SK), 21.09.2018-25.09.2018]
R&D Projects: GA ČR GA17-01251S
Grant - others:ČVUT(CZ) SGS17/193/OHK4/3T/14; GA MŠk(CZ) LM2015042
Institutional support: RVO:67985807
Keywords : Gradient boosting * Random forest * Black-box optimization * Surrogate model * Benchmarking
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-2203/72.pdf
Permanent Link: http://hdl.handle.net/11104/0287361
