Uncertainty in Artificial Intelligence
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Bayesian Multicategory Support Vector Machines
Zhihua Zhang, Michael Jordan
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.
Pages: 552-559
PS Link:
PDF Link: /papers/06/p552-zhang.pdf
AUTHOR = "Zhihua Zhang and Michael Jordan",
TITLE = "Bayesian Multicategory Support Vector Machines",
BOOKTITLE = "Proceedings of the Twenty-Second Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-06)",
ADDRESS = "Arlington, Virginia",
YEAR = "2006",
PAGES = "552--559"

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