A fuzzy model of support vector regression machine

Pei Yi Hao, Jung-Hsien Chiang

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Fuzziness must be considered in systems where available information is uncertain. A model of such a vague phenomenon might be represented as a fuzzy system equation which can be described by the fuzzy functions defined by Zadeh's extension principle. In this paper, we incorporate the concept of fuzzy set theory into the support vector machine (SVM). This integration preserves the benefits of SVM regression model and fuzzy regression model, where the SVM learning theory characterizes properties of learning machines which enable them to generalize well and the fuzzy set theory provides an effective means of capturing the approximate, inexact nature of real world.

Original languageEnglish
Pages (from-to)45-50
Number of pages6
JournalInternational Journal of Fuzzy Systems
Volume9
Issue number1
Publication statusPublished - 2007 Mar 1

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence

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