Application of Support Vector Machine to Pattern Classification

摘要：

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.

作者: Hong Men Yujie Wu Yanchun Gao Xiaoying Li Shanrang Yang

作者单位: School of Automation Engineering,Northeast Dianli University,Jilin Jilin,132012

会议类型: 国际会议

会议名称: 9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-26（万方平台首次上网日期，不代表论文的发表时间）