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Geometrical Selection of Important Inputs with Feedforward Neural Networks

  • F. Rossi
Conference paper

Abstract

In this paper, we introduce a method that allows to evaluate efficiently the ‘importance’ of each coordinate of the input vector of a neural network. This measurement can be used to obtain information about the studied data. It can also be used to suppress irrelevant inputs in order to speed up the classification process conducted by the network.

Keywords

Mutual Information Input Vector Feedforward Neural Network Wavelet Network Irrelevant Input 
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 Wien 1998

Authors and Affiliations

  • F. Rossi
    • 1
    • 2
  1. 1.THOMSON-CSF/AIRSYS/RD/RDTEBagneuxFrance
  2. 2.Universit Paris-IX Dauphine, UFR MDParisFrance

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