Geometrical properties of Nu support vector machines with different norms

Kazushi Ikeda, Noboru Murata

    Research output: Contribution to journalArticle

    17 Citations (Scopus)

    Abstract

    By employing the L1 or L norms in maximizing margins, support vector machines (SVMs) result in a linear programming problem that requires a lower computational load compared to SVMs with the L2 norm. However, how the change of norm affects the generalization ability of SVMs has not been clarified so far except for numerical experiments. In this letter, the geometrical meaning of SVMs with the Lp norm is investigated, and the SVM solutions are shown to have rather little dependency on p.

    Original languageEnglish
    Pages (from-to)2508-2529
    Number of pages22
    JournalNeural Computation
    Volume17
    Issue number11
    DOIs
    Publication statusPublished - 2005 Nov

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    ASJC Scopus subject areas

    • Control and Systems Engineering
    • Artificial Intelligence
    • Neuroscience(all)

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