关键词: 概率近似正确性学习（PAC）-贝叶斯边界, 支持向量机, 模型选择, 泛化性能

Abstract: PAC-Bayes risk bound integrating theories of Bayesian paradigm and structure risk minimization for stochastic classifiers has been considered as a framework for effective evaluating the generalization capability of machine learning algorithms. Aiming at the problem of model selection of SVM, this paper analyzes the theoretical framework of PAC-Bayes bound and its application to SVM, and combines the PAC-Bayes bound with grid search method based on cross validation. A method of model selection based on PAC-Bayes bound（PBB-GS） is put forward to select the best penalty parameter and kernel parameter rapidly. From the experimental results of the UCI datasets, it draws the conclusion that the parameters selected by PBB-GS can make SVM achieve better generalization performance, and this method is simple, fast and accurate, which can improve the model selection of SVM effectively.

Key words: Probably Approximately Correct learning（PAC）-Bayes bound, Support Vector Machine（SVM）, model selection, generalization capability