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A New Model Selection Method for SVM

  • G. Lebrun
  • O. Lezoray
  • C. Charrier
  • H. Cardot
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)

Abstract

In this paper, a new learning method is proposed to build Support Vector Machines (SVMs) Binary Decision Functions (BDF) of reduced complexity and efficient generalization. The aim is to build a fast and efficient SVM classifier. A criterion is defined to evaluate the Decision Function Quality (DFQ) which blendes recognition rate and complexity of a BDF. Vector Quantization (VQ) is used to simplify the training set. A model selection based on the selection of the simplification level, of a feature subset and of SVM hyperparameters is performed to optimize the DFQ. Search space for selecting the best model being huge, Tabu Search (TS) is used to find a good sub-optimal model on tractable times. Experimental results show the efficiency of the method.

Keywords

Support Vector Machine Feature Selection Recognition Rate Tabu Search Feature Subset 
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 Berlin Heidelberg 2006

Authors and Affiliations

  • G. Lebrun
    • 1
  • O. Lezoray
    • 1
  • C. Charrier
    • 1
  • H. Cardot
    • 2
  1. 1.IUT Dépt. SRCLUSAC EA 2607, groupe Vision et Analyse d’ImageSaint-LôFrance
  2. 2.Laboratoire Informatique (EA 2101)Université François-Rabelais de ToursToursFrance

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