• DocumentCode
    314403
  • Title

    Avoiding weight-illgrowth: cascade correlation algorithm with local regularization

  • Author

    Wu, Qingyao ; Nakayama, Kenji

  • Author_Institution
    Graduate Sch. of Nat. Sci. & Tech., Kanazawa Univ., Japan
  • Volume
    3
  • fYear
    1997
  • fDate
    9-12 Jun 1997
  • Firstpage
    1954
  • Abstract
    This paper investigates some possible problems of cascade correlation algorithm, one of which is the zigzag output mapping caused by weight-illgrowth of the adding hidden unit. Without doubt, it could lead to deterioration of the generalization, especially for regression problems. To solve this problem, we combine the cascade correlation algorithm with regularization theory. In addition, some new regularization terms are proposed in light of special cascade structure. Simulation has shown that regularization can smooth the zigzag output, so that the generalization is improved, especially for functional approximation
  • Keywords
    backpropagation; correlation methods; feedforward neural nets; function approximation; generalisation (artificial intelligence); backpropagation; cascade correlation algorithm; feedforward neural networks; functional approximation; generalisation; local regularization; weight-illgrowth; zigzag output mapping; Approximation algorithms; Backpropagation; Computer architecture; Feedforward neural networks; Multi-layer neural network; Network address translation; Network topology; Neural networks; Pattern recognition; Supervised learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks,1997., International Conference on
  • Conference_Location
    Houston, TX
  • Print_ISBN
    0-7803-4122-8
  • Type

    conf

  • DOI
    10.1109/ICNN.1997.614198
  • Filename
    614198