• DocumentCode
    1967950
  • Title

    Boosting in classifier fusion vs. fusing boosted classifiers

  • Author

    Barbu, Costin ; Zhang, Kun ; Peng, Jing ; Buckles, Bill

  • Author_Institution
    Dept. of Electr. Eng. & Comput. Sci., Tulane Univ., New Orleans, LA, USA
  • fYear
    2005
  • fDate
    15-17 Aug. 2005
  • Firstpage
    332
  • Lastpage
    337
  • Abstract
    In this paper we investigate the performance of boosting used for fusing various classifiers. We propose a new boosting - based algorithm for fusion and we show through empirical studies on texture image data sets that it outperforms existing SVM-based classifier fusion technique in terms of accuracy, computational efficiency and robustness.
  • Keywords
    pattern classification; support vector machines; SVM-based classifier fusion technique; classifier fusion booster; image texture; Aggregates; Algorithm design and analysis; Boosting; Computational efficiency; Fusion power generation; Pattern recognition; Robustness; Sampling methods; Training data; Voting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Reuse and Integration, Conf, 2005. IRI -2005 IEEE International Conference on.
  • Print_ISBN
    0-7803-9093-8
  • Type

    conf

  • DOI
    10.1109/IRI-05.2005.1506495
  • Filename
    1506495
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1967950