• DocumentCode
    401647
  • Title

    Separable kernels and its application on model selection

  • Author

    Wu, Tao ; He, Han-gen

  • Author_Institution
    Dept. of Autom. Control, Nat. Univ. of Defense Technol., Changsha, China
  • Volume
    2
  • fYear
    2003
  • fDate
    2-5 Nov. 2003
  • Firstpage
    1279
  • Abstract
    This paper presents some theorems about separable kernels: when a kernel is separable for the given data and how to make a kernel to be separable. Based on these theorems, a new method of kernels parameters´ selection is presented. The advantage of this new method is its efficiency. The experiments have shown that this method can get satisfactory results quickly.
  • Keywords
    operating system kernels; optimisation; parameter estimation; support vector machines; model selection; optimization; parameter selection; separable kernel function; support vector machines; Equations; Helium; Kernel; Linear algebra; Pattern recognition; Statistical learning; Sufficient conditions; Support vector machines; Testing; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2003 International Conference on
  • Print_ISBN
    0-7803-8131-9
  • Type

    conf

  • DOI
    10.1109/ICMLC.2003.1259685
  • Filename
    1259685
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=401647