Mean field variational Bayesian inference for support vector machine classification

Jan Luts and John T. Ormerod

Computational Statistics & Data Analysis, 2014, vol. 73, issue C, 163-176

Abstract: 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.

Keywords: Approximate Bayesian inference; Variable selection; Missing data; Mixed model; Markov chain Monte Carlo (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:73:y:2014:i:c:p:163-176

DOI: 10.1016/j.csda.2013.10.030

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