An efficient method for simplifying decision functions of support vector machines

Jun Guo, Norikazu Takahashi, Tetsuo Nishi

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

A novel method to simplify decision functions of support vector machines (SVMs) is proposed in this paper. In our method, a decision function is determined first in a usual way by using all training samples. Next those support vectors which contribute less to the decision function are excluded from the training samples. Finally a new decision function is obtained by using the remaining samples. Experimental results show that the proposed method can effectively simplify decision functions of SVMs without reducing the generalization capability.

Original languageEnglish
Pages (from-to)2795-2802
Number of pages8
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE89-A
Issue number10
DOIs
Publication statusPublished - Oct 2006
Externally publishedYes

Keywords

  • Complexity
  • Decision function
  • Span
  • Support vector machines

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics

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