• DocumentCode
    508225
  • Title

    Balanced Resampling for Neural Model Selection

  • Author

    Hung, Wen-Liang ; Chuang, Shun-Chin

  • Author_Institution
    Grad. Inst. of Comput. Sci., Nat. Hsinchu Univ. of Educ., Hsinchu, Taiwan
  • Volume
    3
  • fYear
    2009
  • fDate
    14-16 Aug. 2009
  • Firstpage
    597
  • Lastpage
    601
  • Abstract
    In this paper we apply the balanced resampling, which is an efficient bootstrap technique for neural model selection. Our goal is to reduce computer time, so that in the bootstrap procedure, resampling is not done uniformly, this distribution is modified to obtain variance reduction. Efficiency property of this alternative distribution is shown, together with numerical data.
  • Keywords
    bootstrapping; neural nets; sampling methods; balanced resampling; bootstrap technique; neural model selection; Backpropagation; Computer networks; Computer science; Computer science education; Distributed computing; Logistics; Multi-layer neural network; Multilayer perceptrons; Neural networks; Pattern recognition; Asympotic relative efficiency; Balanced resampling; Bootstrap; Neural model selection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation, 2009. ICNC '09. Fifth International Conference on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-0-7695-3736-8
  • Type

    conf

  • DOI
    10.1109/ICNC.2009.586
  • Filename
    5366085
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=508225