Design efficient support vector machine for fast classification

Yiqiang Zhan, Dinggang Shen

Research output: Contribution to journalArticle

63 Citations (Scopus)

Abstract

This paper presents a four-step training method for increasing the efficiency of support vector machine (SVM). First, a SVM is initially trained by all the training samples, thereby producing a number of support vectors. Second, the support vectors, which make the hypersurface highly convoluted, are excluded from the training set. Third, the SVM is re-trained only by the remaining samples in the training set, Finally, the complexity of the trained SVM is further reduced by approximating the separation hypersurface with a subset of the support vectors. Compared to the initially trained SVM by all samples, the efficiency of the finally-trained SVM is highly improved, without system degradation.

Original languageEnglish
Pages (from-to)157-161
Number of pages5
JournalPattern Recognition
Volume38
Issue number1
DOIs
Publication statusPublished - 2005 Jan

Keywords

  • Computational efficiency
  • Support vector machine
  • Training method

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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