关键词: 模糊支持向量机, 隶属度函数, 截集模糊C-均值聚类

Abstract: Fuzzy Support Vector Machine（FSVM），which are the design methods of membership functions are based on class-center，and can effectively overcome the problem that the Support Vector Machine（SVM） is sensitive to the noises and outliers；however，it assigns smaller memberships to the support vectors，which may decrease the effects of these support vectors to the construction of the classification hyperplane.To tackle the above problem，a novel method to determine membership function is proposed.At the same time，the training time of FSVM is generally long which is aroused by the high computational complexity of constructing its kernel function.To reduce the training time of FSVM，the training samples are clustered by an effective sectional set fuzzy C-means clustering（S2FCM） firstly.Then，the cluster centers are taken as training samples.According to the novel method to determine membership function and the S2FCM，a new FSVM is constructed.Experimental results show that the new FSVM can effectively enhance the training speed and classification accuracy rate.

Key words: fuzzy support vector machine, membership function, sectional set fuzzy C-means clustering