Boosting density estimation

Saharon Rosset, Eran Segal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 property applies to this problem as well. We illustrate the potential of this approach through experiments with boosting Bayesian networks to learn density models.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002
PublisherNeural information processing systems foundation
ISBN (Print)0262025507, 9780262025508
StatePublished - 2003
Externally publishedYes
Event16th Annual Neural Information Processing Systems Conference, NIPS 2002 - Vancouver, BC, Canada
Duration: 9 Dec 200214 Dec 2002

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Conference

Conference16th Annual Neural Information Processing Systems Conference, NIPS 2002
Country/TerritoryCanada
CityVancouver, BC
Period9/12/0214/12/02

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