Geometrical properties of Nu support vector machines with different norms

Kazushi Ikeda, Noboru Murata

    研究成果: Article

    17 引用 (Scopus)

    抄録

    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.

    元の言語English
    ページ(範囲)2508-2529
    ページ数22
    ジャーナルNeural Computation
    17
    発行部数11
    DOI
    出版物ステータスPublished - 2005 11

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    Support vector machines
    Linear Programming
    Linear programming
    Support Vector Machine
    Experiments

    ASJC Scopus subject areas

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

    これを引用

    Geometrical properties of Nu support vector machines with different norms. / Ikeda, Kazushi; Murata, Noboru.

    :: Neural Computation, 巻 17, 番号 11, 11.2005, p. 2508-2529.

    研究成果: Article

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