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  Large Scale Transductive SVMs

Collobert, R., Sinz, F., Weston, J., & Bottou, L. (2006). Large Scale Transductive SVMs. The Journal of Machine Learning Research, 7, 1687-1712.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D08D-E Version Permalink: https://hdl.handle.net/21.11116/0000-0004-836C-F
Genre: Journal Article

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http://jmlr.csail.mit.edu/papers/volume7/collobert06a/collobert06a.pdf (Publisher version)
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 Creators:
Collobert, R, Author
Sinz, F1, 2, Author           
Weston, J, Author           
Bottou, L, Author
Affiliations:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: We show how the Concave-Convex Procedure can be applied to the optimization of Transductive SVMs, which traditionally requires solving
a combinatorial search problem. This
provides for the first time a highly scalable algorithm in the nonlinear case.
Detailed experiments verify the utility of our approach.

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 Dates: 2006-08
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 3765
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Title: The Journal of Machine Learning Research
Source Genre: Journal
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Publ. Info: Cambridge, MA : MIT Press
Pages: - Volume / Issue: 7 Sequence Number: - Start / End Page: 1687 - 1712 Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020_1