• DocumentCode
    3517325
  • Title

    Sparse boosting

  • Author

    Xiang, Zhen James ; Ramadge, Peter J.

  • Author_Institution
    Dept. of Electr. Eng., Princeton Univ., Princeton, NJ
  • fYear
    2009
  • fDate
    19-24 April 2009
  • Firstpage
    1625
  • Lastpage
    1628
  • Abstract
    We propose a boosting algorithm that seeks to minimize the AdaBoost exponential loss of a composite classifier using only a sparse set of base classifiers. The proposed algorithm is computationally efficient and in test examples produces composite classifiers that are sparser and generalize as well those produced by Adaboost. The algorithm can be viewed as a coordinate descent method for the l1-regularized Adaboost exponential loss function.
  • Keywords
    learning (artificial intelligence); minimisation; pattern classification; AdaBoost exponential loss minimization; composite classifier; sparse base classifier set; sparse boosting algorithm; Boosting; Classification algorithms; Compressed sensing; Iterative algorithms; Optimization methods; Pattern classification; Signal processing algorithms; Signal representations; Testing; Training data; Algorithms; Optimization methods; Pattern classification; Signal representations;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
  • Conference_Location
    Taipei
  • ISSN
    1520-6149
  • Print_ISBN
    978-1-4244-2353-8
  • Electronic_ISBN
    1520-6149
  • Type

    conf

  • DOI
    10.1109/ICASSP.2009.4959911
  • Filename
    4959911
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3517325