关键词: 支持向量机, BP神经网络, 主成份分析, 经验风险最小, 结构风险最小

Abstract: A new method based on Support Vector Machines with the principle of Structural Risk Minimization are proposed.Theoretical analysis proved,this method can successfully solve the problems in the application of BP neural network including overfit and the danger of getting stuck into local minima,also it avoids the information loss occurred in the application of Primary Component Analysis.Experimental results show that compared to BP Neural Network,the training process of Support Vector Machines is more controllable;and the relative error of evaluation score based on support vector regression machines is smaller;Furthermore,the evaluation differences of the samples are maintained better.

Key words: Support Vector Machine（SVM）, BP Neural Network, Primary Component Analysis（PCA）, empirical risk minimization, structural risk minimization