• DocumentCode
    1559007
  • Title

    Asymptotic convergence of an SMO algorithm without any assumptions

  • Author

    Lin, Chih-Jen

  • Author_Institution
    Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ., Taipei, Taiwan
  • Volume
    13
  • Issue
    1
  • fYear
    2002
  • fDate
    1/1/2002 12:00:00 AM
  • Firstpage
    248
  • Lastpage
    250
  • Abstract
    The asymptotic convergence of C.-J. Lin (2001) can be applied to a modified SMO (sequential minimal optimization) algorithm by S.S. Keerthi et al. (2001) with some assumptions. The author shows that for this algorithm those assumptions are not necessary
  • Keywords
    asymptotic stability; convergence; learning automata; minimisation; SVM; assumptions; asymptotic convergence; modified SMO algorithm; sequential minimal optimization algorithm; support vector machine; Analog computers; Circuits; Constraint optimization; Convergence; Delay effects; Differential equations; Helium; Neural networks; Nonlinear equations; Stability;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/72.977319
  • Filename
    977319
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1559007