Bayesian Multicategory Support Vector Machines
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.
Cite
Text
Zhang and Jordan. "Bayesian Multicategory Support Vector Machines." Conference on Uncertainty in Artificial Intelligence, 2006.Markdown
[Zhang and Jordan. "Bayesian Multicategory Support Vector Machines." Conference on Uncertainty in Artificial Intelligence, 2006.](https://mlanthology.org/uai/2006/zhang2006uai-bayesian/)BibTeX
@inproceedings{zhang2006uai-bayesian,
title = {{Bayesian Multicategory Support Vector Machines}},
author = {Zhang, Zhihua and Jordan, Michael I.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2006},
url = {https://mlanthology.org/uai/2006/zhang2006uai-bayesian/}
}