Generation of Regression Trees using Reinforcement Learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F03%3A03087741" target="_blank" >RIV/68407700:21220/03:03087741 - 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

Generation of Regression Trees using Reinforcement Learning

Original language description

We present a novel methodology for regression trees generation that uses the reinforcement learning frame for learning ef_cient regression trees. We describe the basic variant of such a methodology that uses the Monte-Carlo method to explore the space ofpossible regression trees. Comparison with other methods of regression is performed and evaluated. Our algorithm is implemented as a software program in the JAVA programming language and uses the framework of WEKA machine learning library. This work canbe seen as a step toward on-line learning methodology for generation of decision and regression trees on drifting concepts.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

JC - Computer hardware and software

OECD FORD branch

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Project

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Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2003

Confidentiality

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

Article name in the collection

EUNITE 2003

ISBN

3-86130-194-6

ISSN

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e-ISSN

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Number of pages

5

Pages from-to

262-266

Publisher name

Verlag Mainz

Place of publication

Aachen

Event location

Oulu

Event date

Jul 10, 2003

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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