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  Boosting Algorithms for Maximizing the Soft Margin

Warmuth, M., Glocer, A., & Rätsch, G. (2008). Boosting Algorithms for Maximizing the Soft Margin. In C. Platt, D. Koller, Y. Singer, & S. Roweis (Eds.), Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007 (pp. 1264-1271). Red Hook, NY, USA: Curran.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000C-9E54-4 Version Permalink: https://hdl.handle.net/21.11116/0000-000C-9E56-2
Genre: Conference Paper

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https://proceedings.neurips.cc/paper/2007/file/cfbce4c1d7c425baf21d6b6f2babe6be-Paper.pdf (Publisher version)
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 Creators:
Warmuth, MK, Author
Glocer, AK, Author
Rätsch, G1, Author                 
Affiliations:
1Rätsch Group, Friedrich Miescher Laboratory, Max Planck Society, ou_3378052              

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 Abstract: We present a novel boosting algorithm, called SoftBoost, designed for sets of bi- nary labeled examples that are not necessarily separable by convex combinations of base hypotheses. Our algorithm achieves robustness by capping the distribu- tions on the examples. Our update of the distribution is motivated by minimizing a relative entropy subject to the capping constraints and constraints on the edges of the obtained base hypotheses. The capping constraints imply a soft margin in the dual optimization problem. Our algorithm produces a convex combination of hypotheses whose soft margin is within δ of its maximum. We employ relative en- tropy projection methods to prove an O( ln N δ2 ) iteration bound for our algorithm, where N is number of examples. We compare our algorithm with other approaches including LPBoost, Brown- Boost, and SmoothBoost. We show that there exist cases where the number of iter- ations required by LPBoost grows linearly in N instead of the logarithmic growth for SoftBoost. In simulation studies we show that our algorithm converges about as fast as LPBoost, faster than BrownBoost, and much faster than SmoothBoost. In a benchmark comparison we illustrate the competitiveness of our approach.

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 Dates: 2008-09
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
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Title: Twenty-First Annual Conference on Neural Information Processing Systems (NIPS 2007)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2007-12-03 - 2007-12-06

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Title: Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007
Source Genre: Proceedings
 Creator(s):
Platt, C, Editor
Koller, D, Editor
Singer, Y, Editor
Roweis, ST, Editor
Affiliations:
-
Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1264 - 1271 Identifier: ISBN: 978-1-605-60352-0