Geometrical properties of Nu support vector machines with different norms

Kazushi Ikeda*, Noboru Murata

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


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
Issue number11
Publication statusPublished - 2005 Nov 1

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience


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