关键词: 支持向量机, 支持向量数据描述, 最大间隔最小体积球型支持向量机, 模糊隶属度

Abstract: For multi-class classification, a new hypersphere SVM algorithm is given in this paper, and named as General Maximal-interval Hypersphere SVM（GMHSVM）. In this algorithm, positive and negative samples are separated by two different concentric hyperspheres, the model target is to maximize the difference of two hyperspheres’ radious, which represents the discriminant information of two classes, meanwhile, fuzzy membership is introduced to improve the decision rules of hypersphere SVM, which makes up the voting-decision flaw of two-class classifier. Related properties are obtained by theoretical analysis, simulation experiment results show the effectiveness of the proposed method.

Key words: Support Vector Machine（SVM）, Support Vector Data Description（SVDD）, Maximal-interval Minimal-
volume Hypersphere SVM（MMHSVM）, fuzzy membership