• DocumentCode
    1246101
  • Title

    An improved conjugate gradient scheme to the solution of least squares SVM

  • Author

    Chu, Wei ; Ong, Chong Jin ; Keerthi, S. Sathiya

  • Author_Institution
    Univ. Coll. London, UK
  • Volume
    16
  • Issue
    2
  • fYear
    2005
  • fDate
    3/1/2005 12:00:00 AM
  • Firstpage
    498
  • Lastpage
    501
  • Abstract
    The least square support vector machines (LS-SVM) formulation corresponds to the solution of a linear system of equations. Several approaches to its numerical solutions have been proposed in the literature. In this letter, we propose an improved method to the numerical solution of LS-SVM and show that the problem can be solved using one reduced system of linear equations. Compared with the existing algorithm for LS-SVM, the approach used in this letter is about twice as efficient. Numerical results using the proposed method are provided for comparisons with other existing algorithms.
  • Keywords
    conjugate gradient methods; least squares approximations; minimisation; reduced order systems; support vector machines; conjugate gradient scheme; least square support vector machine; linear equation; reduced system; sequential minimal optimization; Character generation; Equations; Genomics; Iterative algorithms; Kernel; Least squares approximation; Least squares methods; Linear systems; Pattern recognition; Support vector machines; Conjugate gradient (CG); least square support vector machines (LS-SVM); sequential minimal optimization (SMO); Least-Squares Analysis;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/TNN.2004.841785
  • Filename
    1402511