Increasing the efficiency of support vector machine by simplifying the shape of separation hypersurface

  • Authors:

  • Yiqiang Zhan;Dinggang Shen

  • Affiliations:

  • Dept. of Computer Science, The Johns Hopkins University, Baltimore, MD;Center for Computer-Integrated Surgical Systems and Technology

  • Venue:
  • CIS'04 Proceedings of the First international conference on Computational and Information Science
  • Year:
  • 2004

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Abstract

This paper presents a four-step training method for increasing the efficiency of support vector machine (SVM) by simplifying the shape of separation hypersurface. 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.