• DocumentCode
    2707853
  • Title

    Neural-net method for dual subspace pattern recognition

  • Author

    Xu, Lei ; Krzyzak, Adam ; Oja, Erkki

  • Author_Institution
    Centre for Pattern Recognition & Machine Intelligence, Concordia Univ., Montreal, Que., Canada
  • fYear
    1991
  • fDate
    8-14 Jul 1991
  • Firstpage
    379
  • Abstract
    A novel version of the subspace pattern recognition method, called the dual subspace pattern recognition (DSPR) method, is proposed, and a neural-network model with a combination of modified Hebbian and anti-Hebbian learning rules is developed for implementing the DSPR method. An experimental comparison was made on an example data set by using this model and a three-layer forward net with back propagation learning. The results demonstrate that this model can outperform the back propagation model in some applications
  • Keywords
    learning systems; neural nets; pattern recognition; Hebbian learning rules; anti-Hebbian learning rules; back propagation learning; dual subspace pattern recognition; neural-network model; three-layer forward net; Data analysis; Data compression; Data mining; Machine intelligence; Neural networks; Pattern recognition; Personal communication networks; Principal component analysis; Signal processing; Statistical analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
  • Conference_Location
    Seattle, WA
  • Print_ISBN
    0-7803-0164-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.1991.155363
  • Filename
    155363