Research on fuzzy twin support vector machinebased on hybrid fuzzy membership
基于混合模糊隶属度的模糊双支持向量机研究

As a new version of support vector machineSVM, twin support vector machineTWSVM is proposed recently. TWSVM is not only more faster than a conventional SVM, but shows good generalization for pattern classification. But the different effects of the different training samples on the classification hyperplanes are ignored in TWSVM, and the limitation is existed for some actual applications. Therefore, this paper presented a fuzzy twin support vector machine based on hybrid fuzzy membership. It designed a fuzzy membership function combined distance with affinity, and modified TWSVM by applying the fuzzy membership to every training sample. Finally it built two optimal nonparallel hyperplanes to achieve classification. The experiments indicate that the classification performance of the algorithm is more superiorer than a traditional TWSVM.