Mehran Sahami, Susan Dumais, David Heckerman, Eric Horvitz  Home/Search
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Abstract: In addressing the growing problem of junk E-mail on the Internet, we examine methods for the automated construction of filters to eliminate such unwanted messages from a user's mail stream. By casting this problem in a decision theoretic framework, we are able to make use of probabilistic learning methods in conjunction with a notion of differential misclassification cost to produce filters which are especially appropriate for the nuances of this task. While this may appear, at first, to be a... (Update)
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BibTeX entry: (Update)
Mehran Sahami, Susan Dumais, David Heckerman, and Eric Horvitz. A bayesian approach to filtering junk e-mail. In AAAI-98 Workshop on Learning for Text Categorization, 1998. http://citeseer.ist.psu.edu/sahami98bayesian.html More
@inproceedings{ sahami98bayesian,
author = "Mehran Sahami and Susan Dumais and David Heckerman and Eric Horvitz",
title = "A Bayesian Approach to Filtering Junk {E}-Mail",
booktitle = "Learning for Text Categorization: Papers from the 1998 Workshop",
publisher = "AAAI Technical Report WS-98-05",
address = "Madison, Wisconsin",
year = "1998",
url = "citeseer.ist.psu.edu/sahami98bayesian.html" }
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1291 The Nature of Statistical Learning Theory (context) - Vapnik - 1995 ACM
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1 Text categorization with support vector machines: Learning w.. (context) - networks, of et al. - 1997
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