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Abstract: . Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper we evaluate approaches for inducing classifiers from data, based on the theory of learning Bayesian networks. These networks are factored... (Update)
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BibTeX entry: (Update)
Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning (this volume). http://citeseer.ist.psu.edu/friedman97bayesian.html More
@article{ friedman97bayesian,
author = "Nir Friedman and Dan Geiger and Moises Goldszmidt",
title = "Bayesian Network Classifiers",
journal = "Machine Learning",
volume = "29",
number = "2-3",
pages = "131-163",
year = "1997",
url = "citeseer.ist.psu.edu/friedman97bayesian.html" }
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