• DocumentCode
    2971738
  • Title

    Training strategies for weightless neural networks

  • Author

    Ludermir, Teresa B. ; de Olivereira, W.R.

  • Author_Institution
    Dept. de Inf., Univ. Federal de Pernambuco, Recife, Brazil
  • Volume
    3
  • fYear
    1993
  • fDate
    25-29 Oct. 1993
  • Firstpage
    2731
  • Abstract
    Weightless neural networks (WNN) are implemented as random access memories. Training WNN requires only global error signals. WNN simulations can learn significantly faster than learning by error-backpropagation. The aim of this paper is to discuss different training strategies for WNN. One new strategy is suggested.
  • Keywords
    learning (artificial intelligence); neural nets; probabilistic automata; random-access storage; cut point node; global error signals; learning; probabilistic automata; random access memories; weightless neural networks; Acoustic propagation; Artificial neural networks; Character recognition; Computational modeling; Computer networks; Formal languages; Neural networks; Neurons; Random access memory; Read-write memory;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
  • Print_ISBN
    0-7803-1421-2
  • Type

    conf

  • DOI
    10.1109/IJCNN.1993.714288
  • Filename
    714288
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2971738