计算机应用研究2013，Vol.30Issue(2)：432-435,4.DOI:10.3969/j.issn.1001-3695.2013.02.031
基于混合模糊隶属度的模糊双支持向量机研究
Research on fuzzy twin support vector machine based on hybrid fuzzy membership
摘要
Abstract
As a new version of support vector machine(SVM) ,twin support vector machine( TWSVM) 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.关键词
模糊隶属度/支持向量机/双支持向量机/模式分类Key words
fuzzy membership/ support vector machine/ twin support vector machine/ pattern classification分类
信息技术与安全科学引用本文复制引用
丁胜锋,孙劲光..基于混合模糊隶属度的模糊双支持向量机研究[J].计算机应用研究,2013,30(2):432-435,4.基金项目
辽宁省重点实验室资助项目(2008s115) （2008s115）
