• DocumentCode
    3484658
  • Title

    A general formulation for support vector machines

  • Author

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

  • Author_Institution
    Dept. of Mech. Eng., Nat. Univ. of Singapore, Singapore
  • Volume
    5
  • fYear
    2002
  • fDate
    18-22 Nov. 2002
  • Firstpage
    2522
  • Abstract
    In this paper, we derive a general formulation of support vector machines for classification and regression respectively. Le, loss function is proposed as a patch of L1 and L2 soft margin loss functions for classifier, while soft insensitive loss function is introduced as the generalization of popular loss functions for regression. The introduction of the two loss functions results in a general formulation for support vector machines.
  • Keywords
    learning (artificial intelligence); minimisation; pattern classification; quadratic programming; support vector machines; classification; general formulation; minimization problem; quadratic programming; regression; soft insensitive loss function; soft margin loss functions; supervised learning; support vector machines; Hilbert space; Kernel; Lagrangian functions; Mechanical engineering; Quadratic programming; Scholarships; Static VAr compensators; Supervised learning; Support vector machine classification; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
  • Print_ISBN
    981-04-7524-1
  • Type

    conf

  • DOI
    10.1109/ICONIP.2002.1201949
  • Filename
    1201949
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3484658