CFP: IJCAI-01 Workshop on Wrappers for Performance Enhancement in KDD

From: William H. Hsu (bhsu@ringil.cis.ksu.edu)
Date: Tue Jan 02 2001 - 13:46:05 MET

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    FIRST CALL FOR PARTICIPATION
    30 Dec 2000

    IJCAI-2001 Workshop on
    Wrappers for Performance Enhancement in
    Knowledge Discovery in Databases (KDD)
    [workshop code ML-5]

    http://www.kddresearch.org/KDD/Workshops/IJCAI-2001/

    Saturday, 4 Aug 2001
    Seattle, Washington, USA

    WORKSHOP DESCRIPTION

    The rapidly increasing volume of data collected for decision
    support applications in commercial, industrial, medical, and defense
    domains has made it a challenge to scale up knowledge discovery in
    databases (KDD), the machine learning and knowledge acquisition
    component of these applications. Many techniques currently applied
    to KDD admit enhancement through the WRAPPER approach, which uses
    empirical performance of inductive learning algorithms as feedback
    to optimize parameters of the learning system.

    Wrappers include algorithms for performance tuning, especially:
    optimization of learning system parameters (HYPERPARAMETERS) such as
    learning rates and model priors; control of solution size; and change
    of problem representation (or inductive bias optimization).
    Strategies for changing the representation of a machine learning
    problem include decomposition of learning tasks into more tractable
    subproblems; feature construction, or synthesis of more salient or
    useful input variables; and feature subset selection, also known as
    variable elimination (a form of relevance determination).

    This workshop will explore current issues concerning wrapper
    technologies for KDD applications.

    WORKSHOP AUDIENCE

    This workshop is intended for researchers in the area of machine
    learning, including practitioners of knowledge discovery in databases
    (KDD) and statistical and computational learning theorists. Intelligent
    systems researchers with an interest in high-performance computation
    and large-scale, real-world applications of data mining (e.g., inference
    and decision support) will also find this workshop of interest.

    CALL FOR PAPERS

    We encourage submissions containing original theoretical and applied
    concepts in KDD. Experimental results are also encouraged, especially
    on fielded applications, even if they are only preliminary.
    We therefore invite two categories of paper submissions:
            - research papers
            - short summaries (including position papers)

    For the workshop agenda, submission procedure, and up-to-date
    information on the review committee and invited speakers, please
    visit the workshop web site:
    www.kddresearch.org/KDD/Workshops/IJCAI-2001/

    IMPORTANT DATES

    Full Papers due: Friday, 02 Mar 2001
    Short Papers due: Friday, 16 Mar 2001
    acceptance notification: Friday, 30 Mar 2001
    camera-ready copy due: Friday, 13 Mar 2001
    workshop Saturday, 04 Aug 2001

    ORGANIZING COMMITTEE

        William H. Hsu (primary contact)
        Kansas State University

        Hillol Kargupta
        Washington State University

        Huan Liu
        Arizona State University

        Nick Street
        The University of Iowa

    =======================================================
     William H. Hsu, Ph.D.
     Assistant Professor of CIS, Kansas State University
     Research Scientist, Automated Learning Group, NCSA
     bhsu@cis.ksu.edu, bhsu@ncsa.uiuc.edu
     http://www.cis.ksu.edu/~bhsu ICQ: 28651394
    =======================================================

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    Subject: CFP: IJCAI-01 Workshop on Wrappers for Performance Enhancement in KDD
    Summary: CFP for IJCAI-2001 Workshop on Wrappers for Performance Enhancement in
     KDD
    Keywords: IJCAI-2001, knowledge discovery in databases (KDD), machine learning,
    optimization, data mining, neural, genetic, fuzzy, probabilistic, workshop

    FIRST CALL FOR PARTICIPATION
    30 Dec 2000

    IJCAI-2001 Workshop on
    Wrappers for Performance Enhancement in
    Knowledge Discovery in Databases (KDD)
    [workshop code ML-5]

    http://www.kddresearch.org/KDD/Workshops/IJCAI-2001/

    Saturday, 4 Aug 2001
    Seattle, Washington, USA

    WORKSHOP DESCRIPTION

    The rapidly increasing volume of data collected for decision
    support applications in commercial, industrial, medical, and defense
    domains has made it a challenge to scale up knowledge discovery in
    databases (KDD), the machine learning and knowledge acquisition
    component of these applications. Many techniques currently applied
    to KDD admit enhancement through the WRAPPER approach, which uses
    empirical performance of inductive learning algorithms as feedback
    to optimize parameters of the learning system.

    Wrappers include algorithms for performance tuning, especially:
    optimization of learning system parameters (HYPERPARAMETERS) such as
    learning rates and model priors; control of solution size; and change
    of problem representation (or inductive bias optimization).
    Strategies for changing the representation of a machine learning
    problem include decomposition of learning tasks into more tractable
    subproblems; feature construction, or synthesis of more salient or
    useful input variables; and feature subset selection, also known as
    variable elimination (a form of relevance determination).

    This workshop will explore current issues concerning wrapper
    technologies for KDD applications.

    WORKSHOP AUDIENCE

    This workshop is intended for researchers in the area of machine
    learning, including practitioners of knowledge discovery in databases
    (KDD) and statistical and computational learning theorists. Intelligent
    systems researchers with an interest in high-performance computation
    and large-scale, real-world applications of data mining (e.g., inference
    and decision support) will also find this workshop of interest.

    CALL FOR PAPERS

    We encourage submissions containing original theoretical and applied
    concepts in KDD. Experimental results are also encouraged, especially
    on fielded applications, even if they are only preliminary.
    We therefore invite two categories of paper submissions:
            - research papers
            - short summaries (including position papers)

    For the workshop agenda, submission procedure, and up-to-date
    information on the review committee and invited speakers, please
    visit the workshop web site:
    www.kddresearch.org/KDD/Workshops/IJCAI-2001/

    IMPORTANT DATES

    Full Papers due: Friday, 02 Mar 2001
    Short Papers due: Friday, 16 Mar 2001
    acceptance notification: Friday, 30 Mar 2001
    camera-ready copy due: Friday, 13 Mar 2001
    workshop Saturday, 04 Aug 2001

    ORGANIZING COMMITTEE

        William H. Hsu (primary contact)
        Kansas State University

        Hillol Kargupta
        Washington State University

        Huan Liu
        Arizona State University

        Nick Street
        The University of Iowa

    =======================================================
     William H. Hsu, Ph.D.
     Assistant Professor of CIS, Kansas State University
     Research Scientist, Automated Learning Group, NCSA
     bhsu@cis.ksu.edu, bhsu@ncsa.uiuc.edu
     http://www.cis.ksu.edu/~bhsu ICQ: 28651394
    =======================================================

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