• DocumentCode
    1818226
  • Title

    Cresceptron: a self-organizing neural network which grows adaptively

  • Author

    Weng, John ; Ahuja, Narendra ; Huang, Thomas S.

  • Author_Institution
    Beckman Inst., Illinois Univ., Urbana, IL, USA
  • Volume
    1
  • fYear
    1992
  • fDate
    7-11 Jun 1992
  • Firstpage
    576
  • Abstract
    Cresceptron uses a hierarchical framework to grow neural networks automatically, adaptively, and incrementally through learning. At every level of the hierarchy, new concepts are detected automatically and the network grows by creating new neurons and synapses which memorize the new concepts and their context. The training samples are generalized to other perceptually equivalent items through hierarchical tolerance of deviation. The neural network recognizes the learned items and their variations by hierarchically associating the learned knowledge with the input. It segments the recognized items from the input through back training along the response paths
  • Keywords
    hierarchical systems; learning (artificial intelligence); neural nets; self-adjusting systems; Cresceptron; hierarchical framework; neural networks; self-organizing; training samples; Backpropagation; Electric breakdown; Humans; Input variables; Learning systems; Neural networks; Neurons; Unsupervised learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1992. IJCNN., International Joint Conference on
  • Conference_Location
    Baltimore, MD
  • Print_ISBN
    0-7803-0559-0
  • Type

    conf

  • DOI
    10.1109/IJCNN.1992.287150
  • Filename
    287150