%0 Journal Article %A RAO Gang %A LIU Qiongsun %T Sample reduction strategy for support vector machines based on fisher discriminant analysis %D 2012 %R %J Computer Engineering and Applications %P 156-157 %V 48 %N 3 %X The paper presents a strategy of reducing the size of the training sample set for Support Vector Machines(SVM). This strategy extracts the potential support vectors using the method of Fisher discriminant analysis, which forms the new training sample set used in SVM. The results of simulation experiments show effective reduction for large-scale training sample set and improvement of operation efficiency of this algorithm, guaranteeing the classification precision. %U http://cea.ceaj.org/EN/abstract/article_27931.shtml