The LCCP for Optimizing Kernel Parameters for SVM

  • Sabri Boughorbel
  • Jean Philippe Tarel
  • Nozha Boujemaa
Conference paper

DOI: 10.1007/11550907_93

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3697)
Cite this paper as:
Boughorbel S., Tarel J.P., Boujemaa N. (2005) The LCCP for Optimizing Kernel Parameters for SVM. In: Duch W., Kacprzyk J., Oja E., Zadrożny S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg

Abstract

Tuning hyper-parameters is a necessary step to improve learning algorithm performances. For Support Vector Machine classifiers, adjusting kernel parameters increases drastically the recognition accuracy. Basically, cross-validation is performed by sweeping exhaustively the parameter space. The complexity of such grid search is exponential with respect to the number of optimized parameters. Recently, a gradient descent approach has been introduced in[1] which reduces drastically the search steps of the optimal parameters. In this paper, we define the LCCP (Log Convex Concave Procedure) optimization scheme derived from the CCCP (Convex ConCave Procedure) for optimizing kernel parameters by minimizing the radius-margin bound. To apply the LCCP, we prove, for a particular choice of kernel, that the radius is log convex and the margin is log concave. The LCCP is more efficient than gradient descent technique since it insures that the radius margin bound decreases monotonically and converges to a local minimum without searching the size step. Experimentations with standard data sets are provided and discussed.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sabri Boughorbel
    • 1
  • Jean Philippe Tarel
    • 2
  • Nozha Boujemaa
    • 1
  1. 1.IMEDIA Group, INRIA RocquencourtLe ChesnayFrance
  2. 2.DESE, LCPCParisFrance

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