• DocumentCode
    2834794
  • Title

    A modified ν-SV method for simplified regression

  • Author

    Shilton, A. ; Palaniswami, M.

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia
  • fYear
    2004
  • fDate
    2004
  • Firstpage
    422
  • Lastpage
    427
  • Abstract
    In the present paper we describe a new algorithm for support vector regression (SVR). Like standard ν-SVR algorithms, this algorithm automatically adjusts the radius of insensitivity (tube width ε) to fit the data. However, this is achieved without additional complexity in the optimisation problem. Moreover, by careful modification of the kernel function, we are able to significantly simplify the form of the dual SVR optimisation problem.
  • Keywords
    computational complexity; optimisation; regression analysis; support vector machines; computational complexity; kernel function; modified ν-algorithm; optimisation; support vector regression; Australia; Cost function; Integrated circuit noise; Kernel; Neural networks; Noise reduction; Pattern recognition; Support vector machines; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Sensing and Information Processing, 2004. Proceedings of International Conference on
  • Print_ISBN
    0-7803-8243-9
  • Type

    conf

  • DOI
    10.1109/ICISIP.2004.1287694
  • Filename
    1287694
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2834794