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  Training a Support Vector Machine in the Primal

Chapelle, O.(2006). Training a Support Vector Machine in the Primal (147). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D23F-E Version Permalink: https://hdl.handle.net/21.11116/0000-0002-87FB-B
Genre: Report

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MPIK-TR-147.pdf (Publisher version), 185KB
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 Creators:
Chapelle, O1, Author           
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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 there is no reason for ignoring it. Moreover, from
the primal point of view, new families of algorithms for large scale SVM
training can be investigated.

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 Dates: 2006-04
 Publication Status: Issued
 Pages: 15
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: 147
BibTex Citekey: 3597
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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
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Pages: - Volume / Issue: 147 Sequence Number: - Start / End Page: - Identifier: -