[UAI] CFPs: special issue on Support Vector Machines

From: Chih-Jen Lin (cjlin@csie.ntu.edu.tw)
Date: Mon Oct 29 2001 - 09:47:33 PST

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                     CALL FOR PAPERS: special issue on SVM

                                   NEUROCOMPUTING

                              An International Journal

        published by Elsevier Science B.V., vol. 42-47, 24 issues, in 2002

            ISNN 0925-2312, URL: http://www.elsevier.nl/locate/neucom

                      Special Issue on Support Vector Machines

                   Paper Submission Deadline: February 28, 2002

    Further information: http://www.csie.ntu.edu.tw/~cjlin/svmcfp.html
        
    Support Vector Machines (SVMs) are currently a very active research area
    within machine learning. Motivated by statistical learning theory
    they have been successfully applied to numerous tasks within data mining,
    computer vision and bioinformatics, for example. SVMs are examples
    of a broader category of learning approaches which utilize the concept
    of kernel substitution, thereby making the task of learning more
    tractable by exploiting an implicit mapping into a high dimensional
    space. SVMs have many appealing properties for machine learning.
    For example, the classic SVM learning task involves quadratic programming:
    there is only one solution and this may be found by using many of the
    efficient algorithms developed in optimization theory. Furthermore,
    recently developed model selection strategies can be applied, so that
    few, if any, learning parameters must be set by the operator. Above
    all, they have been found to work very well in practice.

    The Neurocomputing journal invites original contributions for the
    forthcoming special issue on Support Vector Machines from a broad
    range of areas. Some topics relevant to this special issue include,
    but are not restricted to:

    - -- Theoretical foundations, algorithms, and implementations

    - -- Model selection and hyperparameter tuning

    - -- Choosing kernels for special situations

    - -- Probabilistic treatment of SVMs

    - -- SVM methods for large scale problems

    - -- Benchmarking SVMs against other methods

    - -- Feature selection methods for SVMs

    - -- Key applications including, but not restricted to data mining,
    bioinformatics, text categorization, machine vision, etc.

    Please send two hardcopies of the manuscript before February 28, 2002 to:

    V. David Sanchez A., Neurocomputing - Editor in Chief -
    Advanced Computational Intelligent Systems
    P.O. Box 60130,
    Pasadena, CA 91116-6130, U.S.A.

    Street address:

    1149 Wotkyns Drive
    Pasadena, CA 91103, U.S.A.
    Fax: +1-626-793-5120
    Email: vdavidsanchez@earthlink.net

    including abstract, keywords, a cover page containing the title and
    author names, corresponding author name's complete address including
    telephone, fax, and email address, and clear indication of
    submission to the Special Issue on Support Vector Machines.

    Guest Editors

    Colin Campbell
    Department of Engineering Mathematics
    Bristol University, Bristol BS8 1TR
    United Kingdom
    Phone: (+44) (0) 117 928 9858
    Fax: (+44) (0)117-925-1154
    Email: C.Campbell@bristol.ac.uk

    Chih-Jen Lin
    Department of Computer Science and Information Engineering
    National Taiwan University
    Taipei, Taiwan, 106
    Phone: (+886) 2-2362-5336 x 413
    Fax: (+886) 2-2362-8167
    Email: cjlin@csie.ntu.edu.tw

    S. Sathiya Keerthi
    Department of Mechanical Engineering
    National University of Singapore
    10 KentRidge Crescent
    Singapore 119260
    Republic of Singapore
    Phone: (+65) 874-4684
    Fax: (+65) 779-1459
    Email: mpessk@guppy.mpe.nus.edu.sg

    V. David Sanchez A., Neurocomputing - Editor in Chief -
    Advanced Computational Intelligent Systems
    P.O. Box 60130
    Pasadena, CA 91116-6130
    U.S.A.
    Fax: +1-626-793-5120
    Email: vdavidsanchez@earthlink.net



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