Fast Pattern Selection for Support Vector Classifiers

Training SVM requires large memory and long CPU time when the pattern set is large.To alleviate the computational burden in SVM training,we propose a fast preprocessing algorithm which selects only the patterns near the optimization hyperplane.The experimental results indicate that training time reduction was achieved including the preprocessing,without any loss in classification accuracies when the training set is magnitude.