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A parameter-free non-growing self-organizing map based upon gravitational principles: Algorithm and applications

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Artificial Neural Networks — ICANN 96 (ICANN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1112))

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Abstract

The Kohonen Algorithm was extended by (a) a coupling between node and input space based upon gravitational principles and (b) a controling mechanism based upon two-point correlation functions. The extended algorithm avoids optimization of the learning rate and neighbourhood parameters. Applications are given for both a 3-dimensional example data set with mixed topologies and a 13-dimensional data set of a particle physics experiment.

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Authors and Affiliations

  1. Institut für Kern- und Teilchenphysik, Technische Universität Dresden, D-01062, Dresden, Germany

    J. S. Lange & H. Freiesleben

Authors
  1. J. S. Lange
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  2. H. Freiesleben
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Christoph von der Malsburg Werner von Seelen Jan C. Vorbrüggen Bernhard Sendhoff

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© 1996 Springer-Verlag Berlin Heidelberg

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Lange, J.S., Freiesleben, H. (1996). A parameter-free non-growing self-organizing map based upon gravitational principles: Algorithm and applications. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_139

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  • DOI: https://doi.org/10.1007/3-540-61510-5_139

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61510-1

  • Online ISBN: 978-3-540-68684-2

  • eBook Packages: Springer Book Archive

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