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Neural Self-Organization Using Graphs

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2734))

Abstract

The self-organizing feature map (SOFM) algorithm can be generalized, if the regular neuron grid is replaced by an undirected graph. The training rule is furthermore very simple: after a competition step, the weights of the winner neuron and its neighborhood must be updated. The update is based on the generalized adjacency of the initial graph. This feature is invariant during the training; therefore its derivation can be achieved in the preprocessing. The newly developed self-organizing neuron graph (SONG) algorithm is applied in function approximation, character fitting and satellite image analysis. The results have proven the efficiency of the algorithm.

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Author information

Authors and Affiliations

  1. Department of Photogrammetry and Geoinformatics, Budapest University of Technology and Economics, H-1111 Budapest, Muegyetem rkp. 3, Hungary

    Arpad Barsi

Authors
  1. Arpad Barsi
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Arndtstr. 4, 04275, Leipzig, Germany

    Petra Perner

  2. Center for Automation Research, University of Maryland, College Park, Maryland, 20742-3275, USA

    Azriel Rosenfeld

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© 2003 Springer-Verlag Berlin Heidelberg

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Barsi, A. (2003). Neural Self-Organization Using Graphs. In: Perner, P., Rosenfeld, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2003. Lecture Notes in Computer Science, vol 2734. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45065-3_30

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  • DOI: https://doi.org/10.1007/3-540-45065-3_30

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40504-7

  • Online ISBN: 978-3-540-45065-8

  • eBook Packages: Springer Book Archive

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