摘要
PAC-Bayes理论融合了贝叶斯定理和随机分类器的结构风险最小化原理,它作为一个理论框架,可得到最紧的泛化风险边界。分析了PAC-Bayes理论的研究背景和重要意义,介绍了PAC-Bayes理论框架及其在支持向量机上的应用,分别探讨了多种机器学习算法的PAC-Bayes边界,并特别对非独立同分布数据的PACBayes边界进行了分析。从4个方面深入阐述了PAC-Bayes边界应用的研究现状及进展,并对不同的研究方法和特点进行了比较。最后展望了PAC-Bayes边界未来的研究发展方向。
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.
出处
《计算机科学与探索》
CSCD
北大核心
2015年第1期1-13,共13页
Journal of Frontiers of Computer Science and Technology
基金
国家自然科学基金No.61170177
国家重点基础研究发展计划(973计划)No.2013CB32930X~~
