• DocumentCode
    814547
  • Title

    Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms

  • Author

    Keerthi, S. Sathiya

  • Author_Institution
    Dept. of Mech. Eng., Nat. Univ. of Singapore, Singapore
  • Volume
    13
  • Issue
    5
  • fYear
    2002
  • fDate
    9/1/2002 12:00:00 AM
  • Firstpage
    1225
  • Lastpage
    1229
  • Abstract
    The paper discusses implementation issues related to the tuning of the hyperparameters of a support vector machine (SVM) with L2 soft margin, for which the radius/margin bound is taken as the index to be minimized, and iterative techniques are employed for computing radius and margin. The implementation is shown to be feasible and efficient, even for large problems having more than 10000 support vectors.
  • Keywords
    data analysis; iterative methods; learning automata; minimisation; pattern classification; L2 soft margin; SVM hyperparameter tuning; iterative algorithms; iterative techniques; radius/margin bound; support vector machine; support vectors; Algorithm design and analysis; Helium; Iterative algorithms; Kernel; Large-scale systems; Mechanical engineering; Polynomials; Quadratic programming; Support vector machine classification; Support vector machines;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/TNN.2002.1031955
  • Filename
    1031955
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=814547