• DocumentCode
    2389768
  • Title

    A method for training a feed-forward neural net model while targeting reduced nonlinearity

  • Author

    Koutsougeras, Cris ; Papadourakis, George

  • Author_Institution
    Dept. of Comput. Sci., Tulane Univ., New Orleans, LA, USA
  • fYear
    1991
  • fDate
    10-13 Nov 1991
  • Firstpage
    192
  • Lastpage
    199
  • Abstract
    In the analysis presented for feedforward neural networks, the causes of problems in the adaptation of current models are examined. A new method for training a feedforward neural net model is introduced. The method encompasses elements of both supervised and unsupervised learning. The development of internal representations is no more an issue tangential to the curve fitting objectives of the other known supervised learning methods. Curve fitting remains as a primary objective but unsupervised learning techniques are also used in order to aid the development of internal representations. The net structure is incrementally formed, thus allowing the formation of a structure of reduced nonlinearity
  • Keywords
    learning systems; neural nets; curve fitting; feed-forward neural net model; reduced nonlinearity; supervised learning; training; unsupervised learning; Computer science; Curve fitting; Feedforward neural networks; Feedforward systems; Feeds; Learning systems; Neural networks; Robustness; Sampling methods; Unsupervised learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools for Artificial Intelligence, 1991. TAI '91., Third International Conference on
  • Conference_Location
    San Jose, CA
  • Print_ISBN
    0-8186-2300-4
  • Type

    conf

  • DOI
    10.1109/TAI.1991.167095
  • Filename
    167095