• DocumentCode
    533225
  • Title

    A revised AdaBoost algorithm: FM-AdaBoost

  • Author

    Zhang, Yanfeng ; He, Peikun

  • Author_Institution
    Sch. of Inf. & Electron., Beijing Inst. of Technol., Beijing, China
  • Volume
    11
  • fYear
    2010
  • fDate
    22-24 Oct. 2010
  • Abstract
    In view of ensemble equivalence, this paper proposes a revised AdaBoost algorithm: FM-AdaBoost. It can ensure the ensemble error rates are the least by F-module, which filter classifiers after all of the iteration finish. At the same time, with the optional M-module it can ensure the training error rates decreases monotonously, which improves the training velocity effectively. In the end, simulation results show the algorithm is valid.
  • Keywords
    iterative methods; learning (artificial intelligence); pattern classification; F-module; FM AdaBoost; ensemble error rate; filter classifiers; iteration finish; optional M-module; revised AdaBoost algorithm; training error rates; Accuracy; Algorithm design and analysis; Boosting; Classification algorithms; Error analysis; Training; AdaBoost; classifier; ensemble of classifiers;
  • 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.2010.5623209
  • Filename
    5623209
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=533225