0452709 20240103211443.820160301235959.9 170 s. P cav_un_epca*0452708978-80-01-05841-11-10Praha 2Nakladatelství ČVUT- výroba, Zikova 4, Praha 62015HobzaTomáš Lazy learning local modelling prediction for optimisation cav_un_auth*0324503 Štěch J. CZ 25 cav_un_auth*0101092 Guy Tatiana Valentine Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems 25 Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0324504 Pálková B. CZ 25 cav_un_auth*0101124 Kárný Miroslav Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems 25 Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2015/AS/guy-0452709.pdf GA13-13502S GA ČR cav_un_auth*0292725 The paper addresses a lazy learning (LL) approach to decision making (DM) problem described in fully probabilistic way. The key idea of LL is to simplify the actual DM problem by using past DM problems similar to the current one. The approach can decrease computation complexity and increase quality of learning when no rich alternative information available. The proposed LL approach helps to learn the environment model based on a proximity of the past and current DM problem with Kullback-Leibler divergence serving as a proximity measure. The implemented algorithm is verified on the real data. The results show that the proposed approach improves prediction quality. cav_un_auth*0324318 Stochastic and Physical Monitoring Systems (SPMS2015) Drhleny 22.06.2015-27.06.2015 CZ 2016 BB 4 ZP RVO:67985556 http://hdl.handle.net/11104/0254008 cav_un_auth*0324319 Department of Mathematics FNSPE CTU in Prague Department of Mathematics, Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague CZ S 2015 cav_un_epca*0452708 SPMS 2015 978-80-01-05841-1 1 10 SPMS 2015 Praha 2 Nakladatelství ČVUT- výroba, Zikova 4, Praha 6 2015 Stochastic and Physical Monitoring Systems Hobza Tomáš 340