Lazy Learning of Environment Model from the Past

0452709 - ÚTIA 2016 RIV CZ eng C - Conference Paper (international conference)
Štěch, J. - Guy, Tatiana Valentine - Pálková, B. - Kárný, Miroslav
Lazy Learning of Environment Model from the Past.
SPMS 2015. Praha 2: Nakladatelství ČVUT- výroba, Zikova 4, Praha 6, 2015 - (Hobza, T.), s. 1-10. ISBN 978-80-01-05841-1.
[Stochastic and Physical Monitoring Systems (SPMS2015). Drhleny (CZ), 22.06.2015-27.06.2015]
R&D Projects: GA ČR GA13-13502S
Institutional support: RVO:67985556
Keywords : Lazy learning * local modelling * prediction for optimisation
Subject RIV: BB - Applied Statistics, Operational Research
http://library.utia.cas.cz/separaty/2015/AS/guy-0452709.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0254008