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Abstract: . The naive Bayes classifier, currently experiencing a renaissance in machine learning, has long been a core technique in information retrieval. We review some of the variations of naive Bayes models used for text retrieval and classification, focusing on the distributional assumptions made about word occurrences in documents. 1 Introduction The naive Bayes classifier, long a favorite punching bag of new classification techniques, has recently emerged as a focus of research itself in machine... (Update)
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BibTeX entry: (Update)
Lewis, D. (1998). Naive Bayes at forty: The independence assumption in information retrieval. Conference proceedings of European Conference on Machine Learning (pp. 4--15). http://citeseer.ist.psu.edu/article/lewis98naive.html More
@inproceedings{ lewis98naive,
author = "David D. Lewis",
title = "Naive ({B}ayes) at forty: The independence assumption in information retrieval.",
booktitle = "Proceedings of {ECML}-98, 10th European Conference on Machine Learning",
number = "1398",
publisher = "Springer Verlag, Heidelberg, DE",
address = "Chemnitz, DE",
editor = "Claire N{\'{e}}dellec and C{\'{e}}line Rouveirol",
pages = "4--15",
year = "1998",
url = "citeseer.ist.psu.edu/article/lewis98naive.html" }
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