• DocumentCode
    2486888
  • Title

    Improving boosting performance with a local combination of learners

  • Author

    Mayhua-López, Efraín ; Gómez-Verdejo, Vanessa ; Figueiras-Vidal, Aníbal R.

  • Author_Institution
    Dept. of Signal Theor. & Commun., Univ. Carlos III de Madrid, Leganés, Spain
  • fYear
    2010
  • fDate
    18-23 July 2010
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    This work explores the possibility of improving the performance of Real Adaboost ensemble classifiers by replacing their standard linear combination of learners by a gating scheme. This more powerful fusion method is defined following the epoch-by-epoch construction of boosting ensembles. Preliminary experimental results support the potential of this new approach.
  • Keywords
    learning (artificial intelligence); neural nets; pattern classification; boosting ensembles; boosting performance; epoch-by-epoch construction; gating scheme; real Adaboost ensemble classifiers; standard linear combination; Algorithm design and analysis; Artificial neural networks; Boosting; Cost function; Error analysis; Neurons; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), The 2010 International Joint Conference on
  • Conference_Location
    Barcelona
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-6916-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.2010.5596317
  • Filename
    5596317
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2486888