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Visualizing Hierarchical Representation in a Multilayered Restricted RBF Network

  • Pitoyo Hartono
  • Paul Hollensen
  • Thomas Trappenberg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)

Abstract

In this study we propose a hierarchical neural network that is able to generate a topographical map in its internal layer. The map significantly differs from the conventional Kohonen’s SOM, in that it preserves the topological characteristics in relevance to the context, for example the labels, of the data. This map is useful if we are interested in visualizing the underlying characteristics of the classificability of the data that traditionally cannot be visualized with the standard SOM. In this paper, we expand our network into a multilayered structure that allows us visualize and thus better understand on how the neural network perceives the given data in the light of classification task.

Keywords

Self-Organizing Map Supervised Learning Hierarchical Representation 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Pitoyo Hartono
    • 1
  • Paul Hollensen
    • 2
  • Thomas Trappenberg
    • 2
  1. 1.School of EngineeringChukyo UniversityNagoyaJapan
  2. 2.School of Computer ScienceDalhousie UniversityHalifaxCanada

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