Original title: Lazy Learning of Environment Model from the Past
Authors: Štěch, J. ; Guy, Tatiana Valentine ; Pálková, B. ; Kárný, Miroslav
Document type: Papers
Conference/Event: Stochastic and Physical Monitoring Systems (SPMS2015), Drhleny (CZ), 2015-06-22 / 2015-06-27
Year: 2015
Language: eng
Abstract: 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.
Keywords: Lazy learning; local modelling; prediction for optimisation
Project no.: GA13-13502S (CEP)
Funding provider: GA ČR
Host item entry: SPMS 2015, ISBN 978-80-01-05841-1

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2015/AS/guy-0452709.pdf
Original record: http://hdl.handle.net/11104/0254008

Permalink: http://www.nusl.cz/ntk/nusl-201279


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Research > Institutes ASCR > Institute of Information Theory and Automation
Conference materials > Papers