Using Two-Class Classifiers for Multiclass Classification

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    164 Citations (Scopus)

    Abstract

    The generalization from two-class classification to multiclass classification is not straightforward for discriminants which are not based on density estimation. Simple combining methods use voting, but this has the drawback of inconsequent labelings and ties. More advanced methods map the discriminant outputs to approximate posterior probability estimates and combine these, while other methods use error-correcting output codes. In this paper we want to show the possibilities of simple generalizations of the twoclass classification, using voting and combinations of approximate posterior probabilities.
    Original languageUndefined/Unknown
    Title of host publicationICPR16, Proceedings
    EditorsR Kasturi, D Laurendeau, C Suen
    Place of PublicationLos Alamitos, CA
    PublisherIEEE
    Pages124-127
    Number of pages4
    ISBN (Print)0-7695-1696-3
    Publication statusPublished - 2002
    Event16th International Conference on Pattern Recognition (Quebec City, Canada), vol. II - Los Alamitos, CA
    Duration: 11 Aug 200215 Aug 2002

    Publication series

    Name
    PublisherIEEE Computer Society Press
    NameInternational Conference on Pattern Recognition
    Volume2
    ISSN (Print)1051-4651

    Conference

    Conference16th International Conference on Pattern Recognition (Quebec City, Canada), vol. II
    Period11/08/0215/08/02

    Bibliographical note

    ISSN 1051-4651, phpub 41

    Keywords

    • conference contrib. refereed
    • Conf.proc. > 3 pag

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