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ICANN 98 pp 1009-1013 | Cite as

An Interruptible Connectionist Model for Real-Time Pattern Recognition

  • Jean-Denis Muller
Conference paper
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)

Abstract

We describe an approach used for the conception of neural real-time pattern recognition systems which are interruptible, that is, able to give answers before the computing completion. This method is based on dynamic coding of information in the neural network: the proposed neuron model performs a time integration of its inputs and emits binary spikes trains. We have designed a dynamic shared-weights multi-layer neural classifier with recognition rate close to a more classical pattern recognition network, suitable for real-time applications with early hypothesis production. Because of its low computing complexity, this model seems to be well suited to hardware implementations.

Keywords

Recognition Rate Correct Classification Rate Handwritten Digit Recognition Average Classification Rate Static Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 1998

Authors and Affiliations

  • Jean-Denis Muller
    • 1
  1. 1.CEA-DAMBruyères-le-ChâtelFrance

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