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ICANN ’94 pp 611-614 | Cite as

Learning the Activation Function for the Neurons in Neural Networks

  • G. P. Fletcher
  • C. J. Hinde
Conference paper

Abstract

Ever since the sigmoid replaced the threshold as the main activation function used in artificial neural networks, the properties of the activation function have been largely ignored. Most research aimed at improving the quality of the hypothesis or the speed at which it is obtained has concentrated on the topology or enhancing the learning algorithm (normally back propagation).

Keywords

Artificial Neural Network Activation Function Back Propagation Sigmoid Function Single Neuron 
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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References

  1. Fletcher, G.P., Hinde, C.J., West, A.A. & Williams, D.J., (1994) Neural networks as a paradigm for knowledge elicitation, ibid.Google Scholar
  2. Horejs, J. & Kufudaki, O., (1993) Neural networks with local distributed parameters, Neurocomputing, Vol 5 No 4. pp 211 – 219.CrossRefGoogle Scholar
  3. Rosenblatt, F., (1962) Principles of neurodynamics, Spartan books.MATHGoogle Scholar

Copyright information

© Springer-Verlag London Limited 1994

Authors and Affiliations

  • G. P. Fletcher
    • 1
  • C. J. Hinde
    • 1
  1. 1.Department of Computer StudiesLoughborough UniversityLoughboroughUK

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