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  On the Convergence of Leveraging

Rätsch, G., Mika, S., & Warmuth, M. (2002). On the Convergence of Leveraging. In T. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems 14 (pp. 487-494). Cambridge, MA, USA: MIT Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-DF0F-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-0EB7-E
Genre: Conference Paper

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 Creators:
Rätsch, G1, Author              
Mika, S, Author
Warmuth, MK, Author
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: We give an unified convergence analysis of ensemble learning methods including e.g. AdaBoost, Logistic Regression and the Least-Square-Boost algorithm for regression. These methods have in common that they iteratively call a base learning algorithm which returns hypotheses that are then linearly combined. We show that these methods are related to the Gauss-Southwell method known from numerical optimization and state non-asymptotical convergence results for all these methods. Our analysis includes ℓ1-norm regularized cost functions leading to a clean and general way to regularize ensemble learning.

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 Dates: 2002-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 2184
 Degree: -

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Title: Fifteenth Annual Neural Information Processing Systems Conference (NIPS 2001)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2001-12-03 - 2001-12-08

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Title: Advances in Neural Information Processing Systems 14
Source Genre: Proceedings
 Creator(s):
Dietterich, TG, Editor
Becker, S, Editor
Ghahramani, Z, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 487 - 494 Identifier: ISBN: 0-262-27173-7