Bayesian Multicategory Support Vector Machines

Zhihua Zhang; Michael I. Jordan

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arXiv · 1206.6863

Bayesian Multicategory Support Vector Machines

Zhihua Zhang·Michael I. Jordan

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Abstract

We show that the multi-class support vector machine (MSVM) proposed by Lee et. al. (2004), can be viewed as a MAP estimation procedure under an appropriate probabilistic interpretation of the classifier. We also show that this interpretation can be extended to a hierarchical Bayesian architecture and to a fully-Bayesian inference procedure for multi-class classification based on data augmentation. We present empirical results that show that the advantages of the Bayesian formalism are obtained without a loss in classification accuracy.

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Zhihua Zhang, Michael I. Jordan. 2012-06-27. Bayesian Multicategory Support Vector Machines. https://arxiv.org/abs/1206.6863

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