• DocumentCode
    1685859
  • Title

    Learn++: a classifier independent incremental learning algorithm for supervised neural networks

  • Author

    Polikar, Robi ; Byorick, Jeff ; Krause, Stefan ; Marino, Anthony ; Moreton, Michael

  • Author_Institution
    Electr. & Comput. Eng., Rowan Univ., Glassboro, NJ, USA
  • Volume
    2
  • fYear
    2002
  • fDate
    6/24/1905 12:00:00 AM
  • Firstpage
    1742
  • Lastpage
    1747
  • Abstract
    A versatile incremental learning algorithm is introduced for supervised neural network type classifiers. The proposed algorithm, called Learn++, exploits the synergistic expressive power of an ensemble of weak classifiers for learning additional information from new data. Learn++ is capable of learning new classes, without forgetting previously acquired knowledge, even when the previously used data is no longer available. Furthermore, Learn++ is independent of the specific type of the classifier, and adds the incremental learning capability to any supervised neural network classifier
  • Keywords
    learning (artificial intelligence); neural nets; Learn++; classifier independent incremental learning algorithm; supervised neural network classifier; supervised neural networks; synergistic expressive power; weak classifiers; Availability; Computer networks; Function approximation; Inference algorithms; Machine learning; Neural networks; Pattern recognition; Power engineering and energy; Power engineering computing; Stability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
  • Conference_Location
    Honolulu, HI
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7278-6
  • Type

    conf

  • DOI
    10.1109/IJCNN.2002.1007781
  • Filename
    1007781