Geometrical properties of Nu support vector machines with different norms

Kazushi Ikeda, Noboru Murata

研究成果: Article査読

19 被引用数 (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.

本文言語English
ページ(範囲)2508-2529
ページ数22
ジャーナルNeural Computation
17
11
DOI
出版ステータスPublished - 2005 11 1

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience

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