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  Trading Convexity for Scalability

Collobert, R., Sinz, F., Weston, J., & Bottou, L. (2006). Trading Convexity for Scalability. In W. Cohen, & A. Moore (Eds.), ICML '06: Proceedings of the 23rd International Conference on Machine Learning (pp. 201-208). New York, NY, USA: ACM Press.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D141-E Version Permalink: https://hdl.handle.net/21.11116/0000-0006-C57D-0
Genre: Conference Paper

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https://dl.acm.org/citation.cfm?doid=1143844.1143870 (Publisher version)
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 Creators:
Collobert, R, Author
Sinz, F1, 2, Author           
Weston, J, Author           
Bottou, L, Author
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, 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: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1145/1143844.1143870
BibTex Citekey: 3917
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Title: 23rd International Conference on Machine Learning (ICML 2006)
Place of Event: Pittsburgh, PA, USA
Start-/End Date: 2006-06-25 - 2006-06-29

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Title: ICML '06: Proceedings of the 23rd International Conference on Machine Learning
Source Genre: Proceedings
 Creator(s):
Cohen, W, Editor
Moore, A, Editor
Affiliations:
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Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 201 - 208 Identifier: ISBN: 1-59593-383-2