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Least Squares Support Vector Machine Classifiers

Neural Processing Letters volume 9, pages 293–300 (1999)Cite this article

Abstract

In this letter we discuss a least squares version for support vector machine (SVM) classifiers. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equations, instead of quadratic programming for classical SVM's. The approach is illustrated on a two-spiral benchmark classification problem.

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Authors and Affiliations

  1. Department of Electrical Engineering, Katholieke Universiteit Leuven, ESAT-SISTA Kardinaal Mercierlaan 94, B–3001, Leuven (Heverlee), Belgium, e-mail

    J.A.K. Suykens & J. Vandewalle

Authors
  1. J.A.K. Suykens
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  2. J. Vandewalle
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Suykens, J., Vandewalle, J. Least Squares Support Vector Machine Classifiers. Neural Processing Letters 9, 293–300 (1999). https://doi.org/10.1023/A:1018628609742

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  • DOI: https://doi.org/10.1023/A:1018628609742

  • classification
  • support vector machines
  • linear least squares
  • radial basis function kernel