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Feature Representations in Connectionist Systems

  • John Bankart
  • Philip T. Smith
  • Mark Bishop
  • Paul Minchinton
Chapter
  • 177 Downloads
Part of the Recent Research in Psychology book series (PSYCHOLOGY)

Abstract

This paper has two goals: to demonstrate the importance of the feature representation chosen for a connectionist model, and to examine the properties of a particular model, the back-propagation algorithm, in conditions intended to simulate the ‘graceful degradation’ encountered in human ageing. In a concept learning simulation, the number of zero or near-zero values of the input features and in the responses of the hidden units was found to influence speed of learning, strength of response to a prototype, and performance with distorted input (the latter two being inversely related). Degradation of the network prior to learning enhanced prototype performance but disrupted distortion performance. In the light of these results we discuss the design of efficient learning algorithms and the potentiality of these networks as models for human ageing.

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References

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Copyright information

© Springer-Verlag New York, Inc. 1994

Authors and Affiliations

  • John Bankart
    • 1
  • Philip T. Smith
    • 1
  • Mark Bishop
    • 2
  • Paul Minchinton
    • 2
  1. 1.Department of PsychologyUniversity of ReadingReadingEngland
  2. 2.Department of CyberneticsUniversity of ReadingReadingEngland

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