Support vector machine networks for multi-class classification

Frank Y. Shih, Kai Zhang

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


The support vector machine (SVM) has recently attracted growing interest in pattern classification due to its competitive performance. It was originally designed for two-class classification, and many researchers have been working on extensions to multiclass. In this paper, we present a new framework that adapts the SVM with neural networks and analyze the source of misclassification in guiding our preprocessing for optimization in multiclass classification. We perform experiments on the ORL database and the results show that our framework can achieve high recognition rates.

Original languageEnglish (US)
Pages (from-to)775-786
Number of pages12
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number6
StatePublished - Sep 2005

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


  • Histogram
  • Neural network
  • Pattern classification
  • Support vector machine


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