(Enter summary)
Abstract: This paper empirically compares the performance of di#erent Bayesian models
for text categorization. In particular we examine so-called "sparse" Bayesian
models that explicitly favor simplicity. We present empirical evidence that these
models retain good predictive capabilities while o#ering significant computational
advantages. (Update)
Cited by: More
Journal of Machine Learning Research 5 (2004) 27-72.. - Gert Lanckriet Gert
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BibTeX entry: (Update)
Eyheramendy, S., Genkin, A., Ju, W., Lewis, D. D., and Madigan, D. (2003). Sparse bayesian classifiers for text categorization. Technical report, Department of Statistics, Rutgers University. http://citeseer.ist.psu.edu/article/eyheramendy03sparse.html More
@misc{ eyheramendy03sparse,
author = "S. Eyheramendy and A. Genkin and W. Ju and D. Lewis and D. Madigan",
title = "Sparse bayesian classifiers for text categorization",
text = "Eyheramendy, S., Genkin, A., Ju, W., Lewis, D. D., and Madigan, D. (2003).
Sparse bayesian classifiers for text categorization. Technical report, Department
of Statistics, Rutgers University.",
year = "2003",
url = "citeseer.ist.psu.edu/article/eyheramendy03sparse.html" }
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