Training support vector machines based on stacked generalization for image classification

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Abstract

This paper presents a two-level stacked generalization scheme composed of three generalizers of support vector machines (SVMs) for image classification. They are color, texture, and high-level concept SVMs. The focus of this paper is to investigate two training strategies based on two-fold cross-validation and non-cross-validation for the proposed classification scheme by evaluating their classification performances, margin of the hyperplane and numbers of support vectors of SVMs. The results show that the non-cross-validation training method performs better, having higher correct classification rates, larger margin of the hyperplane, and smaller numbers of support vectors.

Original languageEnglish
Pages (from-to)497-503
Number of pages7
JournalNeurocomputing
Volume64
Issue number1-4 SPEC. ISS.
DOIs
StatePublished - Mar 2005

Keywords

  • Image classification
  • Stacked generalization
  • Support vector machines

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