A fuzzy model of support vector machine regression

Pei Yi Hao, Jung Hsien Chiang

研究成果: Paper

14 引文 斯高帕斯(Scopus)


Fuzziness must be considered in systems where human estimation is influential. 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) regression. The parameters to be identified in SVM regression, such as the components within the weight vector and the bias term, are fuzzy numbers, and the desired outputs in training samples are also fuzzy numbers. 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 the unseen data and the fuzzy set theory might be very useful for finding a fuzzy structure in an evaluation system.

出版狀態Published - 2003 七月 11
事件The IEEE International conference on Fuzzy Systems - St. Louis, MO, United States
持續時間: 2003 五月 252003 五月 28


OtherThe IEEE International conference on Fuzzy Systems
國家United States
城市St. Louis, MO


All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics


Hao, P. Y., & Chiang, J. H. (2003). A fuzzy model of support vector machine regression. 738-742. 論文發表於 The IEEE International conference on Fuzzy Systems, St. Louis, MO, United States.