Hannes Wettig, Peter Grünwald, Teemu Roos, Petri Myllymäki, Henry Tirri  Home/Search
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Abstract: Bayesian network models are widely used for supervised prediction tasks such as classi cation. Usually the parameters of such models are determined using `unsupervised' methods such as maximization of the joint likelihood. In many cases, the reason is that it is not clear how to nd the parameters maximizing the supervised (conditional) likelihood. We show how the supervised learning problem can be solved eciently for a large class of Bayesian network models, including the Naive Bayes (NB) and ... (Update)
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BibTeX entry: (Update)
H. Wettig, P. Grunwald, T. Roos, P. Myllymaki, and H. Tirri. On supervised learning of Bayesian network parameters. Technical Report 2002. http://citeseer.ist.psu.edu/article/wettig02supervised.html More
@misc{ wettig02supervised,
author = "H. Wettig and P. Grunwald and T. Roos and P. Myllymaki and H. Tirri",
title = "On supervised learning of Bayesian network parameters",
text = "H. Wettig, P. Grunwald, T. Roos, P. Myllymaki, and H. Tirri. On supervised
learning of Bayesian network parameters. Technical Report 2002.",
year = "2002",
url = "citeseer.ist.psu.edu/article/wettig02supervised.html" }
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