关键词: 支持向量机, 不确定问题, 模糊理论, 粗糙集

Abstract: It is difficult for support vector machine to deal with uncertain information because SVM is not only sensitive to noises and outliers but also the inconsistence between conditional features and decision labels is not taken into account. In order to overcome the problem, two types of membership are introduced into standard support vector machine, one type of membership is computed by the distance between the training samples and their center as fuzzy membership, the other type of membership is computed by the distance between the training samples and the nearest training sample with different class label as rough membership. At last several comparative experiments are made to show the performance and the validity of the proposed approach.

Key words: support vector machine, uncertain problem, fuzzy theory, rough set