Bayesian Multicategory Support Vector Machines
Machine Learning
2012-07-02 v1 Machine Learning
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.
Keywords
Cite
@article{arxiv.1206.6863,
title = {Bayesian Multicategory Support Vector Machines},
author = {Zhihua Zhang and Michael I. Jordan},
journal= {arXiv preprint arXiv:1206.6863},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI2006)