• DocumentCode
    2286574
  • Title

    Pattern recognition using Boltzmann machine

  • Author

    Ma, Hede

  • Author_Institution
    Eng. Res. Labs., Savannah State Lab., GA, USA
  • fYear
    1995
  • fDate
    26-29 Mar 1995
  • Firstpage
    23
  • Lastpage
    29
  • Abstract
    An approach for pattern recognition using neural networks is proposed. Particularly, a Boltzmann machine, a Hopfield neural net model, is used in pattern recognition with desirable learning ability. The Boltzmann machine features stochastic learning, which acts as the connection dynamics for determining the weights on the connections between the neuron-like cells (processing elements) of different layers in the neural network. An algorithm for pattern recognition using Boltzmann machine is also presented, which could be coded with the C programming language or others to implement the approach for efficient pattern recognition
  • Keywords
    Boltzmann machines; C language; Hopfield neural nets; learning (artificial intelligence); pattern recognition; Boltzmann machine; C programming language; Hopfield neural net model; algorithm; connection dynamics; learning ability; neural networks; neuron-like cells; pattern recognition; processing elements; stochastic learning; Application software; Artificial intelligence; Containers; Humans; Image coding; Pattern analysis; Pattern recognition; Physics computing; Production systems; Real time systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Southeastcon '95. Visualize the Future., Proceedings., IEEE
  • Conference_Location
    Raleigh, NC
  • Print_ISBN
    0-7803-2642-3
  • Type

    conf

  • DOI
    10.1109/SECON.1995.513051
  • Filename
    513051