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Empirical Inference Article 2006

Large Scale Transductive SVMs

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Empirical Inference
Jason Weston

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.

Author(s): Collobert, R. and Sinz, F. and Weston, J. and Bottou, L.
Journal: Journal of Machine Learning Research
Volume: 7
Pages: 1687-1712
Year: 2006
Month: August
Day: 0
Bibtex Type: Article (article)
Digital: 0
Electronic Archiving: grant_archive
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik
Links:

BibTex 

@article{3765,
  title = {Large Scale Transductive SVMs},
  journal = {Journal of Machine Learning Research},
  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.},
  volume = {7},
  pages = {1687-1712},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = aug,
  year = {2006},
  slug = {3765},
  author = {Collobert, R. and Sinz, F. and Weston, J. and Bottou, L.},
  month_numeric = {8}
}