基于PAC-Bayes边界理论的SVM模型选择方法  

Method of SVM model selection based on PAC-Bayes bound theory

作　　者：汤莉[1,2] 赵政[2] 宫秀军[2,3] 

机构地区：[1]天津财经大学理工学院信息科学与技术系,天津300222 [2]天津大学计算机科学与技术学院,天津300072 [3]天津市认知计算与应用重点实验室,天津300072

出　　处：《计算机工程与应用》2015年第6期27-32,共6页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61170177);国家重点基础研究发展规划(973)(No.2013CB32930X);天津大学创新基金;天津财经大学科研项目(No.Q1114)

摘　　要：PAC-Bayes边界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,能有效评价机器学习算法的泛化性能。针对支持向量机(SVM)模型选择问题,通过分析PAC-Bayes边界理论框架及其在SVM上的应用,将PAC-Bayes边界理论与基于交叉验证的网格搜索法相结合,提出一种基于PAC-Bayes边界的SVM模型选择方法(PBB-GS),实现快速优选SVM的惩罚系数和核函数参数。UCI数据集的实验结果表明该方法优选出的参数能使SVM具有较高的泛化性能,并具有简便快速、参数选择准确的优点,能有效改善SVM模型选择问题。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.

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 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]