Lazy Learning of Environment Model from the Past

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F15%3A00452709" target="_blank" >RIV/67985556:_____/15:00452709 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Lazy Learning of Environment Model from the Past

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

BB - Applied statistics, operational research

OECD FORD branch

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Project

<a href="/en/project/GA13-13502S" target="_blank" >GA13-13502S: Fully Probabilistic Design of Dynamic Decision Strategies for Imperfect Participants in Market Scenarios</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2015

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

SPMS 2015

ISBN

978-80-01-05841-1

ISSN

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e-ISSN

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Number of pages

170

Pages from-to

1-10

Publisher name

Nakladatelství ČVUT- výroba, Zikova 4, Praha 6

Place of publication

Praha 2

Event location

Drhleny

Event date

Jun 22, 2015

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

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