• DocumentCode
    1875895
  • Title

    A Parameters Selection Method of SVM

  • Author

    Kou, Deqi ; Zhang, Yuan ; Zheng, Hanyue

  • Author_Institution
    Dept. of Tech. Support Eng., Acad. of Armored Forces Eng., Beijing, China
  • fYear
    2010
  • fDate
    10-12 Dec. 2010
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    An improved artificial fish swarm algorithm called ASFSA is proposed. It could facilitate the selection of values for Step and Visual to meet the balance of algorithm speed and effect. A new SVM parameters selection method based on the ASFSA is described, and the kernel parameter γ and regularization parameter C can both be optimized well. The application case shows that the performance of SVM with optimized parameters is good, so the method is feasible and effective.
  • Keywords
    algorithm theory; support vector machines; ASFSA; SVM parameter selection method; artificial fish swarm algorithm; kernel parameter; regularization parameter; support vector machines; Kernel; Marine animals; Optimization; Support vector machines; Testing; Training; Visualization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Software Engineering (CiSE), 2010 International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-5391-7
  • Electronic_ISBN
    978-1-4244-5392-4
  • Type

    conf

  • DOI
    10.1109/CISE.2010.5676994
  • Filename
    5676994
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1875895