Model Selection for Support Vector Machines via Adaptive Step-Size Tabu Search

  • Gavin C. Cawley


The generalisation properties of a support vector classification network are typically governed by a regularisation parameter, C, and a small number of parameters specifying the kernel function. The process by which the optimal values of these parameters are obtained is known as model selection. This paper describes an automated model selection procedure based on minimisation of an upper bound on the leave-one-out cross-validation error, via a simple tabu search strategy with adaptive step size adjustment.


Support Vector Machine Support Vector Tabu Search Model Selection Procedure Tabu Search Heuristic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • Gavin C. Cawley
    • 1
  1. 1.School of Information SystemsUniversity of East AngliaNorwich, NorfolkUK

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