• DocumentCode
    532444
  • Title

    Expenses estimated for the equipment based on the optimal parameters ε -SVR

  • Author

    Sun Lin-kai ; Jin Jia-shan ; Geng Jun-bao

  • Author_Institution
    Sch. of Naval Archit. & Power, Naval Univ. of Eng., Wuhan, China
  • Volume
    6
  • fYear
    2010
  • fDate
    22-24 Oct. 2010
  • Abstract
    Support vector regression machine (SVR) can establish the expenses estimate model according to the principle of the structure minimum risk. It reaches better accuracy in the small sample estimated, but there are not a set of integrity theories for choosing the parameters of SVR. This paper adopts the methods of varied step to search the optimal parameters. It use the combination accuracy to evaluate the estimated effect, research the influence of the parameters combined form to the estimated accuracy. Then we can assurance the optimized combined form of the parameters. The example indicates the optimized combined form of the parameters can improved the expenses estimated accuracy.
  • Keywords
    equipment evaluation; life cycle costing; regression analysis; risk analysis; support vector machines; expenses estimate model; life cycle cost; optimal parameters ε-SVR; structure minimum risk; support vector regression machine; ε -Support vector regression machine (ε -SVR); expenses estimated; kenal function; optimal parameter;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Application and System Modeling (ICCASM), 2010 International Conference on
  • Conference_Location
    Taiyuan
  • Print_ISBN
    978-1-4244-7235-2
  • Electronic_ISBN
    978-1-4244-7237-6
  • Type

    conf

  • DOI
    10.1109/ICCASM.2010.5620545
  • Filename
    5620545