Boosting Density Estimation
Saharon Rosset, Eran Segal · 2002
Several authors have suggested viewing boosting as a gradient descent search for a good fit in function space. We apply gradient-based boosting methodology to the unsupervised learning problem of density estimation. We show convergence properties of the algorithm and prove that a strength of weak learnability prop-erty applies to this problem as well. We illustrate the potential of this approach through experiments with boosting Bayesian networks to learn density models. 1