A fuzzy model of support vector machine regression

Abstract

Fuzziness must he considered in systems where human estimation is influential. A model of such a vague phenomenon might he represented as a fuzzy system equation which can he 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 he 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 he very useful for finding a fuzzy structure in an evaluation system.