• DocumentCode
    2829565
  • Title

    A Comparative Study of Four Smooth Support Vector Regressions

  • Author

    Shen, Jindong ; Cao, Feilong

  • Author_Institution
    Dept. of Inf. & Math. Sci., China Jiliang Univ., Hangzhou, China
  • fYear
    2009
  • fDate
    19-20 Dec. 2009
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Researching smooth support vector machine for regression (SVR) is an active field in data mining. This paper presents a comparison among four smooth SVRs , epsilon-SSVR, 1st-order polynomial smooth SVR (1PSSVR), 2nd-order polynomial smooth SVR (2PSSVR) and third-order spline smooth SVR (TSSSVR). Accuracy, convergence speed and computational complexity of these regressions are compared.
  • Keywords
    computational complexity; data mining; regression analysis; splines (mathematics); support vector machines; 2nd-order polynomial smooth SVR; computational complexity; convergence speed; data mining; epsilon-SSVR; lst-order polynomial smooth SVR; support vector machine for regression; third-order spline smooth SVR; Computational complexity; Convergence; Data mining; Error correction; Fitting; Mathematical programming; Polynomials; Smoothing methods; Spline; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-4994-1
  • Type

    conf

  • DOI
    10.1109/ICIECS.2009.5364040
  • Filename
    5364040
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2829565