Effects of norms on learning properties of support vector machines

Kazushi Ikeda*, Noboru Murata

*この研究の対応する著者

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

Support Vector Machines (SVMs) are known to have 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 Lp norm instead of the L2 norm. In this paper, we theoretically show the effects of p on the learning properties of SVMs by clarifying its geometrical meaning.

本文言語English
ホスト出版物のタイトル2005 IEEE ICASSP '05 - Proc. - Design and Implementation of Signal Proces.Syst.,Indust. Technol. Track,Machine Learning for Signal Proces. Education, Spec. Sessions
出版社Institute of Electrical and Electronics Engineers Inc.
ページV241-V244
ISBN(印刷版)0780388747, 9780780388741
DOI
出版ステータスPublished - 2005 1月 1
イベント2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA, United States
継続期間: 2005 3月 182005 3月 23

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
V
ISSN(印刷版)1520-6149

Conference

Conference2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
国/地域United States
CityPhiladelphia, PA
Period05/3/1805/3/23

ASJC Scopus subject areas

  • ソフトウェア
  • 信号処理
  • 電子工学および電気工学

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