• DocumentCode
    3250238
  • Title

    A smart algorithm for incremental learning

  • Author

    Wang, Eric Hsiu-Chun ; Kuh, Anthony

  • Author_Institution
    Dept. of Eng.-Econ. Syst., Stanford Univ., CA, USA
  • Volume
    3
  • fYear
    1992
  • fDate
    7-11 Jun 1992
  • Firstpage
    121
  • Abstract
    Incremental learning algorithms not only adjust interconnection weights, but also change the network architecture by adding hidden nodes at the network. The capabilities of these incremental learning algorithms are examined. Four different incremental learning algorithms have been simulated for a variety of learning tasks. To improve the performance of the incremental learning algorithms, a new perceptron learning algorithm is proposed, the smart algorithm, to find the near-optimal set of weights at each node. The simulation results show that the smart algorithm improves the performance of these incremental learning algorithms. Among the four algorithms examined, the global algorithm performed the best
  • Keywords
    learning (artificial intelligence); neural nets; incremental learning; interconnection weights; near-optimal weights set; perceptron learning algorithm; smart algorithm; Bayesian methods; Computer architecture; Curve fitting; Feedforward neural networks; Feedforward systems; Iterative algorithms; Neural networks; Polynomials; Systems engineering and theory; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1992. IJCNN., International Joint Conference on
  • Conference_Location
    Baltimore, MD
  • Print_ISBN
    0-7803-0559-0
  • Type

    conf

  • DOI
    10.1109/IJCNN.1992.227182
  • Filename
    227182