Machine Learning

, Volume 46, Issue 1, pp 131–159

Choosing Multiple Parameters for Support Vector Machines

  • Olivier Chapelle
  • Vladimir Vapnik
  • Olivier Bousquet
  • Sayan Mukherjee

DOI: 10.1023/A:1012450327387

Cite this article as:
Chapelle, O., Vapnik, V., Bousquet, O. et al. Machine Learning (2002) 46: 131. doi:10.1023/A:1012450327387


The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) 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.

support vector machines kernel selection leave-one-out procedure gradient descent feature selection 
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Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Olivier Chapelle
    • 1
  • Vladimir Vapnik
    • 2
  • Olivier Bousquet
    • 3
  • Sayan Mukherjee
    • 4
  1. 1.LIP6ParisFrance
  2. 2.AT&T Research LabsMiddletownUSA
  3. 3.École PolytechniqueFrance
  4. 4.MITCambridgeUSA

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