Mean field variational Bayesian inference for support vector machine classification

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• 作者：Jan Luts ; John T. Ormerod
• 关键词：Approximate Bayesian inference ; Variable selection ; Missing data ; Mixed model ; Markov chain Monte Carlo
• 刊名：Computational Statistics and Data Analysis
• 出版年：May, 2014
• 年：2014
• 卷：73
• 期：Complete
• 页码：163-176
• 全文大小：656 K

文摘

A mean field variational Bayes approach to support vector machines (SVMs) using the latent variable representation on Polson and Scott (2012) is presented. This representation allows circumvention of many of the shortcomings associated with classical SVMs including automatic penalty parameter selection, the ability to handle dependent samples, missing data and variable selection. We demonstrate on simulated and real datasets that our approach is easily extendable to non-standard situations and outperforms the classical SVM approach whilst remaining computationally efficient.