Staged Mixture Modeling and Boosting

Christopher Meek, Bo Thiesson, and David Heckerman

Abstract

In this paper, we introduce and evaluate a data-driven staged mixture modeling technique for building density, regression, and classification models. Our basic approach is to sequentially add components to a finite mixture model using the structural expectation maximization (SEM) algorithm. We show that our technique is qualitatively similar to boosting. This correspondence is a natural byproduct of the fact that we use the SEM algorithm to sequentially fit the mixture model. Finally, in our experimental evaluation, we demonstrate the effectiveness of our approach on a variety of prediction and density estimation tasks using real-world data.

Details

Publication typeInproceedings
Published inProceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence
URLhttp://www.mkp.com/
Pages335-343
NumberMSR-TR-2002-45
InstitutionMicrosoft Research
PublisherMorgan Kaufmann Publishers
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