关键词: 模糊径向基函数网络, 支撑向量机, 约简, 分类

Abstract: A binary classifier based on the Fuzzy Radial Basis Function Network（FRBFN） and SP-V-SVM is presented. The initial architecture of the network is constructed with the sample from dataset. The centers of Gaussian membership functions of each membership variable in the fuzzy layer are determined by the samples randomly extracted in the training data set, whereas the variances depend on the variance of the training data set. The parameters of output layer are accomplished based on the criterion of the max gap between classes. What are more, the nodes of the network sparsity constraints are introduced to realize nodes reduction. The classification tests on several UCI standard data sets are conducted and the results show the effectiveness of the classifier.

Key words: Fuzzy Radial Basis Function Network（FRBFN）, Support Vector Machine（SVM）, reduction, classifier