Geometrical properties of Nu support vector machines with different norms

Kazushi Ikeda; Noboru Murata

doi:10.1162/0899766054796897

Geometrical properties of Nu support vector machines with different norms

Kazushi Ikeda, Noboru Murata

先進理工学部

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 L_p 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
巻	17
号	11
DOI	https://doi.org/10.1162/0899766054796897
出版ステータス	Published - 2005 11 1

ASJC Scopus subject areas

Arts and Humanities (miscellaneous)
Cognitive Neuroscience

文献へのアクセス

10.1162/0899766054796897

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引用スタイル

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