A sequential algorithm for sparse support vector classifiers

Jian Xun Peng, Stuart Ferguson, Karen Rafferty, Victoria Stewart

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Support vector machines (SVMs), though accurate, are not preferred in applications requiring high classification speed or when deployed in systems of limited computational resources, due to the large number of support vectors involved in the model. To overcome this problem we have devised a primal SVM method with the following properties: (1) it solves for the SVM representation without the need to invoke the representer theorem, (2) forward and backward selections are combined to approach the final globally optimal solution, and (3) a criterion is introduced for identification of support vectors leading to a much reduced support vector set. In addition to introducing this method the paper analyzes the complexity of the algorithm and presents test results on three public benchmark problems and a human activity recognition application. These applications demonstrate the effectiveness and efficiency of the proposed algorithm.

Original languageEnglish
Pages (from-to)1195-1208
Number of pages13
JournalPattern Recognition
Issue number4
Publication statusPublished - Apr 2013


  • Support vector classifier; Sequential algorithm; Sparse design

ASJC Scopus subject areas

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


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