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  Large Scale Transductive SVMs

Collobert, R., Sinz, F., Weston, J., & Bottou, L. (2006). Large Scale Transductive SVMs. Journal of Machine Learning Research, 7, 1687-1712. Retrieved from http://jmlr.csail.mit.edu/papers/volume7/collobert06a/collobert06a.pdf.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D08D-E Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D08E-C
Genre: Journal Article

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Collobert, R, Author
Sinz, F1, Author           
Weston, J2, Author           
Bottou, L, Author
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: 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
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 Identifiers: URI: http://jmlr.csail.mit.edu/papers/volume7/collobert06a/collobert06a.pdf
BibTex Citekey: 3765
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Title: Journal of Machine Learning Research
Source Genre: Journal
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Pages: - Volume / Issue: 7 Sequence Number: - Start / End Page: 1687 - 1712 Identifier: -