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Choosing Multiple Parameters for Support Vector Machines




The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVM) is considered. This is done by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters. Usual methods for choosing parameters, based on exhaustive search become intractable as soon as the number of parameters exceeds two. Some experimental results assess the feasibility of our approach for a large number of parameters (more than 100) and demonstrate an improvement of generalization performance.

Author(s): Chapelle, O. and Vapnik, V. and Bousquet, O. and Mukherjee, S.
Journal: Machine Learning
Volume: 46
Number (issue): 1
Pages: 131-159
Year: 2002
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Choosing Multiple Parameters for Support Vector Machines},
  author = {Chapelle, O. and Vapnik, V. and Bousquet, O. and Mukherjee, S.},
  journal = {Machine Learning},
  volume = {46},
  number = {1},
  pages = {131-159},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2002}