Browsing Cognitive & Neural Systems by Subject "Fast learning"

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Browsing Cognitive & Neural Systems by Subject "Fast learning"

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  • Carpenter, Gail (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2000-09)
    Adaptive resonance is a theory of cognitive information processing which has been realized as a family of neural network models. In recent years, these models have evolved to incorporate new capabilities in the cognitive, ...
  • Carpenter, Gail A.; Grossberg, Stephen; Rosen, David (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-02)
    This article introduces ART 2-A, an efficient algorithm that emulates the self-organizing pattern recognition and hypothesis testing properties of the ART 2 neural network architecture, but at a speed two to three orders ...
  • Carpenter, Gail A.; Milenova, Boriana L.; Noeske, Benjamin W. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1997-12)
    Distributed coding at the hidden layer of a multi-layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off-line learning to avoid ...
  • Carpenter, Gail A. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1996-01)
    A class of adaptive resonance theory (ART) models for learning, recognition, and prediction with arbitrarily distributed code representations is introduced. Distributed ART neural networks combine the stable fast learning ...
  • Kopco, Norbert; Carpenter, Gail (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2000-03)
    This study presents an analysis of a modified ARTMAP neural network in which a graded signal function replaces the standard choice-by-difference function. The modifications are introduced mathematically and the performance ...
  • Carpenter, Gail (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2000-10)

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