关键词: PAC-Bayes边界, 支持向量机, 泛化能力, 分类器

Abstract: PAC-Bayes theory integrating theories of Bayesian paradigm and structure risk minimization for stochastic classifiers has been considered as a framework for deriving some of the tightest generalization bounds. This paper analyzes the research background and profound significance of PAC-Bayes theory, and introduces the framework of PAC-Bayes theory and its application to support vector machine (SVM). Then, this paper discusses PAC-Bayes bound of many machine learning algorithms, and specially analyzes the bound with the non-IID data. Furthermore, this paper elaborates research status and development of the PAC-Bayes bound application from four directions, and compares different research methods and features. Finally, this paper draws the research prospect of the PAC-Bayes bound.

Key words: PAC-Bayes bound, support vector machine (SVM), generalization capability, classifier