Adaboost is consistent

Bartlett, Peter L. & Traskin, Mikhail (2007) Adaboost is consistent. Journal of Machine Learning Research, 8, pp. 2347-2368.

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The risk, or probability of error, of the classifier produced by the AdaBoost algorithm is investigated. In particular, we consider the stopping strategy to be used in AdaBoost to achieve universal consistency. We show that provided AdaBoost is stopped after n1-ε iterations---for sample size n and ε ∈ (0,1)---the sequence of risks of the classifiers it produces approaches the Bayes risk.

Impact and interest:

47 citations in Scopus
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30 citations in Web of Science®

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Full-text downloads:

617 since deposited on 18 Aug 2011
4 in the past twelve months

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ID Code: 44014
Item Type: Journal Article
Refereed: Yes
Additional Information: The published version is also freely available via the Official URL.
Keywords: boosting, adaboost, consistency, OAVJ
ISSN: 1533-7928
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > Mathematical Sciences
Copyright Owner: Copyright 2007 Peter L. Bartlett and Mikhail Traskin.
Deposited On: 18 Aug 2011 03:17
Last Modified: 29 Feb 2012 14:34

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