Mean field variational Bayesian inference for support vector machine classification

13 May 2013 Jan Luts John T. Ormerod

A mean field variational Bayes approach to support vector machines (SVMs) using the latent variable representation on Polson & 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... (read more)

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