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 |
巻 | 17 |
号 | 11 |
DOI | |
出版ステータス | Published - 2005 11月 1 |
ASJC Scopus subject areas
- 人文科学(その他)
- 認知神経科学