Stage Mixture Modeling and Boosting
In this paper, we introduce and evaluate a data-driven staged mixture modeling technique for building density, regression, and classi cation models. Our basic approach is to sequentially add components to a nite 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 t the mixture model. Finally, in our experimental evaluation, we demonstrate the e ectiveness of our approach on a variety of prediction and density estimation tasks using real-world data.