关键词: 核学习, 支持向量机, 模糊支持向量机, Fisher判别准则

Abstract: The advantage of a kernel method often depends critically on a proper choice of the kernel function.A promising approach is to learn the kernel from data automatically.In this paper，a novel method is proposed for learning the kernel matrix based on maximizing a class separability criterion that is similar to those used by Linear Discriminant Analysis（LDA） and Kernel Fisher Discriminant（KFD）.This paper proposes this approach when FSVM is used for multiclassification task.And the results of experiments on face data set shows that the method is feasible.

Key words: kernel learning, Support Vector Machine（SVM）, Fuzzy Support Vector Machine（FSVM）, Fisher discriminant criterion