Learning properties of support vector machines with p-norm

Kazushi Ikeda*, Noboru Murata


研究成果: Conference article査読

2 被引用数 (Scopus)


Support Vector Machines (SVMs) are a new classification technique which has a high generalization ability, yet a heavy computational load since margin maximization results in a quadratic programming problem. It is known that this maximization task results in a pth-order programming problem if we employ the p-norm instead of the Euclidean norm, that is. When p = 1, for example, it is a linear programming problem with a much lower computational load. In this article, we theoretically show that p has very little affect on the generalization performance of SVMs in practice by considering its geometrical meaning.

ジャーナルMidwest Symposium on Circuits and Systems
出版ステータスPublished - 2004
イベントThe 2004 47th Midwest Symposium on Circuits and Systems - Conference Proceedings - Hiroshima, Japan
継続期間: 2004 7月 252004 7月 28

ASJC Scopus subject areas

  • 電子材料、光学材料、および磁性材料
  • 電子工学および電気工学


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