Boosting Density Estimation
Abstract
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.
Cite
Text
Rosset and Segal. "Boosting Density Estimation." Neural Information Processing Systems, 2002.Markdown
[Rosset and Segal. "Boosting Density Estimation." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/rosset2002neurips-boosting/)BibTeX
@inproceedings{rosset2002neurips-boosting,
title = {{Boosting Density Estimation}},
author = {Rosset, Saharon and Segal, Eran},
booktitle = {Neural Information Processing Systems},
year = {2002},
pages = {657-664},
url = {https://mlanthology.org/neurips/2002/rosset2002neurips-boosting/}
}