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.
Partially supported by MURST grant 9903244848 and MM09308497.
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References
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.
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.
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.
C. Goller. A Connectionist Approach for Learning Search-Control Heuristics for Automated Deduction Systems. PhD thesis, Technical University Munich, Computer Science, 1997.
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.
Teuvo Kohonen. Self-Organisation and Associative Memory. Springer, 3rd edition, 1990.
A. Sperduti and A. Starita. Supervised neural networks for classification of structures. IEEE Trans Neural Networks, Vol. 8(No. 3):714-735, 1997.
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© 2001 Springer-Verlag London Limited
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Hagenbuchner, M., Tsoi, A.C., Sperduti, A. (2001). A Supervised Self-Organizing Map for Structured Data. In: Advances in Self-Organising Maps. Springer, London. https://doi.org/10.1007/978-1-4471-0715-6_4
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DOI: https://doi.org/10.1007/978-1-4471-0715-6_4
Publisher Name: Springer, London
Print ISBN: 978-1-85233-511-3
Online ISBN: 978-1-4471-0715-6
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