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Convergence rate of minimization learning for neural networks

  • Marghny H. Mohamed
  • Teruya Minamoto
  • Koichi Niijima
Neural Networks
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1398)

Abstract

In this paper, we present the convergence rate of the error in a neural network which was learnt by a constructive method. The constructive mechanism is used to learn the neural network by adding hidden units to this neural network. The main idea of this work is to find the eigenvalues of the transformation matrix concerning the error before and after adding hidden units in the neural network. By using the eigenvalues, we show the relation between the convergence rate in neural networks without and with thresholds in the output layer.

Keywords

Neural Network Hide Layer Convergence Rate Output Layer Connection Weight 
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.

References

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Marghny H. Mohamed
    • 1
  • Teruya Minamoto
    • 1
  • Koichi Niijima
    • 1
  1. 1.Department of InformaticsKyushu UniversityFukuoka, KasukaJapan

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