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《Journal of Xi'an University of Arts & Science(Natural Science Edition)》 2008-01
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A New Fuzzy Supportive Vector Machine Algorithm

CHEN Xiao-juan,LIU San-yang,MAN Ying,LIU Kui(Dept.of Mathematics,Xidian University,Xi'an 710071,China)  
SVM is very sensitive to noises and outliers when solving the problem of classification,a new method of membership function is put forward.This paper studies and analyzes the present Fuzzy Supportive Vector Machine method in view of these questions.In these methods,the design of membership function is a key to solve the classification problem with FSVM.This paper mainly studies membership function.Based on the center vector method,a new membership method is provided.This approach greatly improves the classification accuracy and generalization.Experiment results have confirmed its effectiveness and feasibility.
【Fund】: 国家自然科学基金资助项目(60674108)
【CateGory Index】: TP18
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