  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.
