Dual membership SVM method based on spectral clustering

作     者：Xiaodong Song Liyan Han 

作者机构：School of Economics and ManagementBeihang UniversityBeijing 100191P.R.China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2012年第23卷第2期

页      面：225-232页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0825[工学-航空宇航科学与技术] 

基　　金：supported by the National Natural Science Foundation of China (70831001 70821061) 

主　　题：dual membership model fuzzy support vector ma- chine (FSVM) spectral clustering sample "overlap". 

摘      要：A new fuzzy support vector machine algorithm with dual membership values based on spectral clustering method is pro- posed to overcome the shortcoming of the normal support vector machine algorithm, which divides the training datasets into two absolutely exclusive classes in the binary classification, ignoring the possibility of ＂overlapping＂ region between the two training classes. The proposed method handles sample ＂overlap＂ effi- ciently with spectral clustering, overcoming the disadvantages of over-fitting well, and improving the data mining efficiency greatly. Simulation provides clear evidences to the new method.