摘要
双支持向量机是近年提出的一种新的支持向量机。在处理模式分类问题时,双支持向量机速度远远超过传统支持向量机,而且显示出较好的推广能力。但双支持向量机没有考虑不同输入样本点可能会对分类超平面的形成产生不同影响,在某些实际问题中具有局限性。为了克服这个缺点,提出了一种基于模糊隶属度的双支持向量机。该算法设计了一种基于距离的模糊隶属度函数,给不同的训练样本赋予不同的模糊隶属度,构建两个最优非平行超平面,最终实现二值分类。实验结果表明,这种改进双支持向量机的分类性能优于传统的双支持向量机。
As a new version of support vector machine(SVM),twin support vector machine(TWSVM) was proposed recently.TWSVM is not only 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,a twin support vector machine based on fuzzy membership was presented.A fuzzy membership function based on distance was designed,and TWSVM was modified by applying the fuzzy membership to every training sample,finally two optimal nonparallel hyperplanes were builded to achieve classification.The experiment results indicate that the classification performance of the algorithm is more superiorer than a traditional TWSVM.
出处
《辽宁石油化工大学学报》
CAS
2012年第4期76-79,82,共5页
Journal of Liaoning Petrochemical University
关键词
双支持向量机
模糊隶属度
模式分类
Twin support vector machine
Fuzzy membership
Pattern classification
