A learning algorithm for improving the classification speed of support vector machines

Jun Guo, Norikazu Takahashi, Tetsuo Nishi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

Abstract

A novel method for training support vector machines (SVMs) is proposed to speed up the SVMs in test phase. It has three main steps. First, an SVM is trained on all the training samples, thereby producing a number of support vectors. Second, the support vectors, which contribute less to the shape of the decision surface, are excluded from the training set. Finally, the SVM is re-trained only on the remaining samples. Compared to the initially trained SVM, the efficiency of the finally trained SVM is highly improved, without system degradation.

Original languageEnglish
Title of host publicationProceedings of the 2005 European Conference on Circuit Theory and Design
Pages381-384
Number of pages4
DOIs
Publication statusPublished - Dec 1 2005
Externally publishedYes
Event2005 European Conference on Circuit Theory and Design - Cork, Ireland
Duration: Aug 28 2005Sep 2 2005

Publication series

NameProceedings of the 2005 European Conference on Circuit Theory and Design
Volume3

Other

Other2005 European Conference on Circuit Theory and Design
CountryIreland
CityCork
Period8/28/059/2/05

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Guo, J., Takahashi, N., & Nishi, T. (2005). A learning algorithm for improving the classification speed of support vector machines. In Proceedings of the 2005 European Conference on Circuit Theory and Design (pp. 381-384). [1523140] (Proceedings of the 2005 European Conference on Circuit Theory and Design; Vol. 3). https://doi.org/10.1109/ECCTD.2005.1523140