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  Training a Support Vector Machine in the Primal

Chapelle, O. (2007). Training a Support Vector Machine in the Primal. In Large Scale Kernel Machines (pp. 29-50). Cambridge, MA, USA: MIT Press.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CC0B-D Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CC0C-B
Genre: Book Chapter

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 Creators:
Chapelle, O1, Author           
Bottou, Editor
L., Editor
Chapelle, O., Editor
DeCoste, D., Editor
Weston, J., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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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: 2007-09
 Publication Status: Issued
 Pages: -
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 Identifiers: URI: http://mitpress.mit.edu/catalog/item/default.asp?ttype=2tid=11268mode=toc
BibTex Citekey: 4178
 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: 29 - 50 Identifier: -