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  Regularizing AdaBoost

Rätsch, G., Onoda, T., & Müller, K. (1999). Regularizing AdaBoost. Advances in Neural Information Processing Systems, 564-570.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-E69B-8 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-E69C-6
Genre: Conference Paper

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 Creators:
Rätsch, G1, Author           
Onoda, T, Author
Müller, KR, Author
Kearns, M., Editor
Solla, S., Editor
Cohn, D., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: Boosting methods maximize a hard classification margin and are known as powerful techniques that do not exhibit overfitting for low noise cases. Also for noisy data boosting will try to enforce a hard margin and thereby give too much weight to outliers, which then leads to the dilemma of non-smooth fits and overfitting. Therefore we propose three algorithms to allow for soft margin classification by introducing regularization with slack variables into the boosting concept: (1) AdaBoost reg and regularized versions of (2) linear and (3) quadratic programming AdaBoost. Experiments show the usefulness of the proposed algorithms in comparison to another soft margin classifier: the support vector machine.

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 Dates: 1999-06
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 0-262-11245-0
URI: http://books.nips.cc/nips11.html
BibTex Citekey: 2186
 Degree: -

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Title: Twelfth Annual Conference on Neural Information Processing Systems (NIPS 1998)
Place of Event: Denver, CO, USA
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Title: Advances in Neural Information Processing Systems
Source Genre: Journal
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Affiliations:
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 564 - 570 Identifier: -