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Generation of Regression Trees using Reinforcement Learning

The result's identifiers

  • 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

  • DOI - Digital Object Identifier

Alternative languages

  • 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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    JC - Computer hardware and software

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    Z - Vyzkumny zamer (s odkazem do CEZ)

Others

  • Publication year

    2003

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    EUNITE 2003

  • ISBN

    3-86130-194-6

  • ISSN

  • e-ISSN

  • 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