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2005
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Efficient discriminative learning of Bayesian network classifier via boosted augmented naive Bayes

10 years 3 months ago
Efficient discriminative learning of Bayesian network classifier via boosted augmented naive Bayes
The use of Bayesian networks for classification problems has received significant recent attention. Although computationally efficient, the standard maximum likelihood learning method tends to be suboptimal due to the mismatch between its optimization criteria (data likelihood) and the actual goal for classification (label prediction). Recent approaches to optimizing the classification performance during parameter or structure learning show promise, but lack the favorable computational properties of maximum likelihood learning. In this paper we present the Boosted Augmented Naive Bayes (BAN) classifier. We show that a combination of discriminative data-weighting with generative training of intermediate models can yield a computationally efficient method for discriminative parameter learning and structure selection.
Yushi Jing, Vladimir Pavlovic, James M. Rehg
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2005
Where ICML
Authors Yushi Jing, Vladimir Pavlovic, James M. Rehg
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