Training Set Selection of SVM Based on SVC

When used in large-scale data classification,large memory will be occupied by training support vector machine,and the optimization is very slow.In this paper,a novel training set selection method is presented after describing the principle of SVM.The corresponding weight scheme is also presented,which is to choose the penalty term according to the number of support vector of each class,thus to optimize the separating hyper-plane and to improve the classification accurate ratio.The simulation results show that the training set obtained from this method is very representative with a high classification efficiency and little time consumption.