%0 Journal Article %T A New Fuzzy Membership Assignment Approach for Fuzzy Svm Based on Adaptive Pso in Classification Problems %A Almasi, Omid Naghash %A Gooqeri, Hamed Sadeghi %A Asl, Behnam Soleimanian %A Tang, Wan Mei %J Journal of Mathematics and Computer Science %D 2015 %V 14 %N 2 %@ ISSN 2008-949X %F Almasi2015 %X Noises will confuse Support Vector Machine (SVM) in the training phase. To overcome this problem, SVM was extended to Fuzzy SVM (FSVM) by incorporating an appropriate fuzzy membership to each data point. Thus, how to choose a proper fuzzy membership is of paramount importance in FSVM. In this paper, Adaptive Particle Swarm Optimization (APSO) method minimizes the generalization error by changing the attributes values of positive and negative class centers to make them free of attribute-noise. As the APSO converged, the fuzzy memberships are assigned for each training data points based on their distance to the corresponding purified class centers with the same class-label. To demonstrate the effectiveness of the proposed FSVM, its performance on artificial and real-world data sets is compared with three FSVM algorithms in the literature. %9 journal article %R 10.22436/jmcs.014.02.08 %U http://dx.doi.org/10.22436/jmcs.014.02.08 %P 171-182 %0 Book %T Statistical learning theory %A V. N. Vapnik %D 1998 %I New York: Wiley %C %F Vapnik1998 %0 Journal Article %T Automatic capacity tuning of very large VC-dimension classifier %A I. Guyon %A B. Boser %A V. Vapnik %J Adv. Neural Inform. Process. Systems %D 1993 %V 5 %F Guyon1993 %0 Journal Article %T An overview of statistical learning theory %A V. N. Vapnik %J IEEE Transaction on Neural Networks %D 1999 %V 10(5) %F Vapnik1999 %0 Journal Article %T A tutorial on support vector machines for pattern recognition %A C. J. C. 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