• DocumentCode
    396712
  • Title

    Relating Bayesian learning to training in recurrent networks

  • Author

    Spiegel, Rainer

  • Author_Institution
    Dept. of Comput., London Univ., UK
  • Volume
    2
  • fYear
    2003
  • fDate
    20-24 July 2003
  • Firstpage
    908
  • Abstract
    It is demonstrated that a recurrent neural network relying on an error correcting learning algorithm and a localist coding scheme is able to converge to a solution that would be expected from Bayesian learning. This is possible even without implementing Bayes theorem and without assigning prior probabilities to the model.
  • Keywords
    Bayes methods; error correction; learning (artificial intelligence); recurrent neural nets; Bayesian learning; error correcting learning algorithm; localist coding; neural network; recurrent networks; Bayesian methods; Educational institutions; Error correction; Intelligent networks; Neural networks; Probability; Psychology; Recurrent neural networks; Statistics; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2003. Proceedings of the International Joint Conference on
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7898-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2003.1223811
  • Filename
    1223811
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=396712