基于类中心和密切度的L-2范数FSVM  

L-2 Norm FSVM Based on Combining Cluster Center with Affinity

作　　者：李雷[1] 杨真真[1] 杨永鹏[2] 

机构地区：[1]南京邮电大学理学院,江苏南京210046 [2]南京邮电大学电子科学与工程学院,江苏南京210046

出　　处：《南京邮电大学学报（自然科学版）》2010年第6期34-37,42,共5页Journal of Nanjing University of Posts and Telecommunications：Natural Science Edition

基　　金：国家自然科学基金(10371106;10471114;61070234;61071167);江苏省高校自然科学基金(04KJB110097;08KJB520003);南京邮电大学攀登计划(NY207064)资助项目

摘　　要：支持向量机(SVM)是解决回归问题的一种有效的方法,但传统的支持向量机对样本中的噪声和孤立点非常敏感。为了克服这个问题,文中提出了一种基于类中心和密切度的L-2范数模糊支持向量机(L-2范数FSVM),即模糊隶属度的建立不仅根据样本到类中心的距离,而且根据样本点和其目标点之间的密切度。仿真实验结果显示了该方法有效地减少了噪声的影响,改进了回归的精度,增强了推广能力。Support vector machine（SVM） is an effective method for resolving regression problem,however,traditional SVM is very sensitive to noises and outliers in the training sample.In order to overcome this problem,L-2 norm fuzzy support vector machine（L-2 norm FSVM） based on combining cluster center with affinity is proposed in this paper.The fuzzy membership is defined not only by the distance between a point and its cluster center,but also by two different points of the sample,which is depicted as the affinity between them.Simulation results for artificial data show that the proposed method gives good performance on reducing the effects of noise and improves the regression accuracy and generalization.

关 键 词：L-2范数模糊支持向量机(L-2范数FSVM) 模糊隶属度函数 类中心 密切度 信息几何 

分 类 号：TN911.7[电子电信—通信与信息系统]