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  Trading Convexity for Scalability

Collobert, R., Sinz, F., Weston, J., & Bottou, L. (2006). Trading Convexity for Scalability. Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), 201-208.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D141-E Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D142-C
Genre: Conference Paper

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 Creators:
Collobert, R, Author
Sinz, F1, Author           
Weston, J2, Author           
Bottou, L, Author
Cohen A. Moore, W. W., Editor
Affiliations:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: Convex learning algorithms, such as Support Vector Machines (SVMs), are often seen as highly desirable because they offer strong practical properties and are amenable to theoretical analysis. However, in this work we show how non-convexity can provide scalability advantages over convexity. We show how concave-convex programming can be applied to produce (i) faster SVMs where training errors are no longer support vectors, and (ii) much faster Transductive SVMs.

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 Dates: 2006-06
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: URI: http://www.icml2006.org/icml2006/home.html
DOI: 10.1145/1143844.1143870
BibTex Citekey: 3917
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Title: 23rd International Conference on Machine Learning
Place of Event: Pittsburgh, PA, USA
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Title: Proceedings of the 23rd International Conference on Machine Learning (ICML 2006)
Source Genre: Journal
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Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 201 - 208 Identifier: -