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Interpretable neural networks with BP-SOM

  • Ton Weijters
  • Antal van den Bosch
  • Jaap van den Herik
Neural Networks
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1398)

Abstract

Interpretation of models induced by artificial neural networks is often a difficult task. In this paper we focus on a relatively novel neural network architecture and learning algorithm, bp-som that offers possibilities to overcome this difficulty. It is shown that networks trained with BP-SOM show interesting regularities, in that hidden-unit activations become restricted to discrete values, and that the som part can be exploited for automatic rule extraction.

Keywords

Hide Layer Learning Algorithm Hide Unit Neural Network Architecture Rule Extraction 
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 Berlin Heidelberg 1998

Authors and Affiliations

  • Ton Weijters
    • 1
  • Antal van den Bosch
    • 2
  • Jaap van den Herik
    • 3
  1. 1.Information TechnologyEindhoven University of TechnologyThe Netherlands
  2. 2.ILK / Computational LinguisticsTilburg UniversityThe Netherlands
  3. 3.Department of Computer ScienceUniversiteit MaastrichtThe Netherlands

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