Advertisement

A Supervised Self-Organizing Map for Structured Data

  • Markus Hagenbuchner
  • Ah Chung Tsoi
  • Alessandro Sperduti

Abstract

A self organizing map (SOM) for processing of structured data, using an unsupervised learning approach, called SOM-SD, has recently been proposed. Here, we suggest a new version of SOM, using the supervised learning approach. We compare the supervised version and the unsupervised version of SOM-SD on a benchmark problem involving visual patterns. As may be expected, the supervised version is able to solve the classification problem using very compact networks.

Keywords

Root Node Leaf Node Directed Acyclic Graph Target Class Winning Neuron 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    A.M. Bianucci, A. Micheli, A. Sperduti, and A. Starita. Analysis of the internal representations developed by neural networks for structures applied to qsar studies of benzodiazepines. Journal of Chemical Information and Computer Sciences, 41(1):202-218, 2001.Google Scholar
  2. 2.
    Horst Bunke and Alberto Sanfeliu, editors. Syntactic and Structural Pattern Recognition: Theory and Applications. Series in Computer science; v. 7. Singapore; New Jersey: World Scientific, c1990. ISBN-9971505525.MATHGoogle Scholar
  3. 3.
    Paolo Frasconi, Marco Gori, and Alessandro Sperduti. A general framework for adaptive processing in data structures. In IEEE Trans on Neural Networks, volume Vol 9, pages 768-785, 1998.CrossRefGoogle Scholar
  4. 4.
    C. Goller. A Connectionist Approach for Learning Search-Control Heuristics for Automated Deduction Systems. PhD thesis, Technical University Munich, Computer Science, 1997.Google Scholar
  5. 5.
    Markus Hagenbuchner and Ah Chung Tsoi. The traffic policeman benchmark. In Michel Verleysen, editor, European Symposium on Artificial Neural Networks, ISBN 2-9600049-9-X, pages 63-68. D-Facto, April 1999.Google Scholar
  6. 6.
    Teuvo Kohonen. Self-Organisation and Associative Memory. Springer, 3rd edition, 1990.Google Scholar
  7. 7.
    A. Sperduti and A. Starita. Supervised neural networks for classification of structures. IEEE Trans Neural Networks, Vol. 8(No. 3):714-735, 1997.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2001

Authors and Affiliations

  • Markus Hagenbuchner
    • 1
  • Ah Chung Tsoi
    • 1
  • Alessandro Sperduti
    • 2
  1. 1.Faculty of InformaticsUniversity of WollongongAustralia
  2. 2.Dip. di InformaticaUniversità di PisaPisaItaly

Personalised recommendations