Christopher Meek, Bo Thiesson, and David Heckerman
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.
|Published in||Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence|
|Publisher||Morgan Kaufmann Publishers|
All copyrights reserved by Morgan Kaufmann Publishers 2002.