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  Training a Support Vector Machine in the Primal

Chapelle, O. (2007). Training a Support Vector Machine in the Primal. In L. Bottou, O. Chapelle, D. DeCoste, & J. Weston (Eds.), Large Scale Kernel Machines (pp. 29-50). Cambridge, MA, USA: MIT Press.

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Chapelle-Training.pdf (Any fulltext), 250KB
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Chapelle, O1, 2, Author              
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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Most literature on Support Vector Machines (SVMs) concentrate on the dual optimization problem. In this paper, we would like to point out that the primal problem can also be solved efficiently, both for linear and non-linear SVMs, and that there is no reason to ignore this possibility. On the contrary, from the primal point of view new families of algorithms for large scale SVM training can be investigated.

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 Dates: 2007-08
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: 4178
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Title: Large Scale Kernel Machines
Source Genre: Book
 Creator(s):
Bottou, L, Editor
Chapelle, O1, Editor            
DeCoste, D, Editor
Weston, J, Editor            
Affiliations:
1 Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795            
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 29 - 50 Identifier: ISBN: 0-262-25579-0

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Title: Neural information processing series
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