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Empirical Inference Article 2005

A tutorial on v-support vector machines

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Empirical Inference
Bernhard Schölkopf
  • Director

We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), and kernel feature spaces. We place particular emphasis on a description of the so-called -SVM, including details of the algorithm and its implementation, theoretical results, and practical applications. Copyright © 2005 John Wiley & Sons, Ltd.

Author(s): Chen, P-H. and Lin, C-J. and Schölkopf, B.
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Journal: Applied Stochastic Models in Business and Industry
Volume: 21
Number (issue): 2
Pages: 111-136
Year: 2005
Day: 0
Bibtex Type: Article (article)
Digital: 0
Electronic Archiving: grant_archive
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

BibTex 

@article{3353,
  title = {A tutorial on v-support vector machines},
  journal = {Applied Stochastic Models in Business and Industry},
  abstract = {We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), and kernel feature spaces. We place particular emphasis on a description of the so-called -SVM, including details of the algorithm and its implementation, theoretical results, and practical applications. Copyright © 2005 John Wiley & Sons, Ltd.},
  volume = {21},
  number = {2},
  pages = {111-136},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2005},
  slug = {3353},
  author = {Chen, P-H. and Lin, C-J. and Sch{\"o}lkopf, B.}
}