Ranking Pattern Recognition Features for Neural Networks

  • Wenjia Wang
  • Phillis Jones
  • Derek Partridge
Conference paper


In all pattern recognition technologies, a crucial question is what features to extract and use for the basis of a recognition system. There are well-established techniques for ranking features to be used in linear combination, but when non-linear combination technologies, such as neural networks, are contemplated feature significance ranking is more problematic. In this paper we present a practical technique for ranking features in terms of significance for a neural-net pattern recognizer. We provide the results of applying this clamping technique to a small selection of problems, both well-defined abstract problems that permit a precise exploration of the technology, and more realistic data-defined problems that demonstrate its practical worth.


Neural Network Input Feature Generalisation Rate Redundant Feature Trained 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 Limited 1999

Authors and Affiliations

  • Wenjia Wang
    • 1
  • Phillis Jones
    • 1
  • Derek Partridge
    • 1
  1. 1.Neural Computing Research Group, Department of Computer ScienceUniversity of ExeterUK

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