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Abstract: Bayesian approaches to supervised learning use priors on the classifier parameters. However, few priors aim at achieving "sparse" classifiers, where irrelevant/redundant parameters are automatically set to zero. Two well-known ways of obtaining sparse classifiers are: use a zero-mean Laplacian prior on the parameters, and the "support vector machine" (SVM). Whether one uses a Laplacian prior or an SVM, one still needs to specify/estimate the parameters that control the degree of sparseness of... (Update)
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BibTeX entry: (Update)
M. Figueiredo and A. K. Jain. Bayesian learning of sparse classifiers. In CVPR2001. http://citeseer.ist.psu.edu/figueiredo01bayesian.html More
@misc{ figueiredo-bayesian,
author = "M. Figueiredo and A. Jain",
title = "Bayesian learning of sparse classifiers",
text = "M. Figueiredo and A. K. Jain. Bayesian learning of sparse classifiers.
In CVPR2001.",
url = "citeseer.ist.psu.edu/figueiredo01bayesian.html" }
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