Application of Support Vector Machine to Pattern Classification

Support vector machine (SVM) is applied for classification in this paper. The SVM operates on the principle of structure risk minimization; hence better generalization ability is guaranteed. This paper discussed the basic principle of the SVM at first, and then we chose SVM classifier with polynomial kernel and the Gaussian radial basis function kernel (RBFSVM) to recognize the cancer samples (benign and malignant). Selecting some value for parameters to know different performance each parameter produces to outputs. The simulations of the recognizing of two class samples have been presented and discussed. Results show the RBF SVM can classify complicated patterns and achieve higher recognition rate. SVM overcomes disadvantages of the artificial neural networks. The results indicate that the SVM classifier exhibits good generalization performance and the recognition rate above 93.33% for the testing samples. This means the support vector machines are effective for classification.