A Bayesian approach to support vector machines for the binary classification

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• 作者：Jiangsheng Yu ; Fei Cheng ; Huilin Xiong ; Wanling Qu ; Xue-wen Chen
• 关键词：Bayesian method ; Classification ; Support vector machine (SVM) ; Kernel ; Feature space ; Reversible jump Markov chain Monte Carlo (RJMCMC) ; Gaussian mixture model
• 刊名：Neurocomputing
• 出版年：2008
• 出版时间：December 2008
• 年：2008
• 卷：72
• 期：1-3
• 页码：177-185
• 全文大小：687 K

文摘

The model of support vector machine (SVM) has been widely used to solve the problems of regression/classification. Here we propose a Bayesian approach to determining the separating hyperplane of an SVM, once its maximal margin is determined in the traditional way. This novel method minimizes the Bayes error in some derived direction. In the proposed model of b-SVM, all the parameters are estimated by the reversible jump Markov chain Monte Carlo (RJMCMC) strategies, and the location parameter of decision boundary is finally described by a posterior distribution. Tested by many independent random experiments of 2-fold cross validations, the experimental results on some high-throughput biodata sets demonstrate the promising performance and robustness of this novel classification method.