• DocumentCode
    3614101
  • Title

    Multi-class support vector machine

  • Author

    V. Franc;V. Hlavac

  • Author_Institution
    Fac. of Electr. Eng., Czech Tech. Univ., Karlovo, Czech Republic
  • Volume
    2
  • fYear
    2002
  • fDate
    6/24/1905 12:00:00 AM
  • Firstpage
    236
  • Abstract
    We propose a transformation from the multi-class support vector machine (SVM) classification problem to the single-class SVM problem which is more convenient for optimization. The proposed transformation is based on simplifying the original problem and employing the Kesler construction which can be carried out by the use of properly defined kernel only. The experiments conducted indicate that the proposed method is comparable with the one-against-all decomposition solved by the state-of-the-art sequential minimal optimizer algorithm.
  • Keywords
    "Support vector machines","Support vector machine classification","Kernel","Training data","Cost function","Cybernetics","Optimization methods"
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition, 2002. Proceedings. 16th International Conference on
  • ISSN
    1051-4651
  • Print_ISBN
    0-7695-1695-X
  • Type

    conf

  • DOI
    10.1109/ICPR.2002.1048282
  • Filename
    1048282