The Comparison With improved mixture kernel SVM and traditional Neural Network

摘要：

Support Vector Machines bases on statistical learning theory and replace the minimization experiential risk minimization by structural risk minimization, thus have large advantage over the tradition al neu ral network on small sample set for classification. Related documents and experimental data prove that SVM is the best learning machine among all kinds recently and has large advantage over those of traditional neural networks. In this paper we prove that the performance of an improved SVM with mixed kernel will make the advantage more obviously. Different from some papers choose kernels and parameters randomly, we choose the kernels for SVM theoretically, through observing and computing the kernel matrix. Base on this, we used the selected kernel functions to get a new mixed kernel function. Experiential data proved that this new SVM has a better performance than that of that traditional neural network. Tbis will give us a method to get a new learning machine for pattern identification

关键词： support vector machine neural network kernel function kernel matrix mixed kernel function

作者: ZHU SHU-XIAN ZHU XUE-LI

作者单位: Mechanical and Electrical Engineering Department SuZhou University of Science and Technology SuZhou, China

会议类型: 国际会议

会议名称: 2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)

会议地点: 大连

会议语种:英文

页码: 629-631

在线出版日期: 2010-07-05（万方平台首次上网日期，不代表论文的发表时间）