Saharon Rosset, Eran Segal
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.