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  Trading Convexity for Scalability

Collobert, R., Sinz, F., Weston, J., & Bottou, L. (2007). Trading Convexity for Scalability. In Large Scale Kernel Machines (pp. 275-300). Cambridge, MA, USA: MIT Press.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CC09-2 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CC0A-F
Genre: Book Chapter

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 Creators:
Collobert, R, Author
Sinz, F1, Author           
Weston, J2, Author           
Bottou, L, Author
Bottou, Editor
L., Editor
Chapelle, O., Editor
DeCoste, D., Editor
Weston, J., 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 nonconvexity 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: 2007-09
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: URI: http://mitpress.mit.edu/catalog/item/default.asp?ttype=2tid=11268
BibTex Citekey: 4435
 Degree: -

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Title: Large Scale Kernel Machines
Source Genre: Book
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Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 275 - 300 Identifier: -