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IJCAI
2003

When Discriminative Learning of Bayesian Network Parameters Is Easy

11 years 1 months ago
When Discriminative Learning of Bayesian Network Parameters Is Easy
Bayesian network models are widely used for discriminative prediction tasks such as classification. Usually their parameters are determined using 'unsupervised' methods such as maximization of the joint likelihood. The reason is often that it is unclear how to find the parameters maximizing the conditional (supervised) likelihood. We show how the discriminative learning problem can be solved efficiently for a large class of Bayesian network models, including the Naive Bayes (NB) and treeaugmented Naive Bayes (TAN) models. We do this by showing that under a certain general condition on the network structure, the discriminative learning problem is exactly equivalent to logistic regression with unconstrained convex parameter spaces. Hitherto this was known only for Naive Bayes models. Since logistic regression models have a concave log-likelihood surface, the global maximum can be easily found by local optimization methods.
Hannes Wettig, Peter Grünwald, Teemu Roos, Pe
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where IJCAI
Authors Hannes Wettig, Peter Grünwald, Teemu Roos, Petri Myllymäki, Henry Tirri
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