Summary
Three extensions of the traditional learning rule for Self-Organizing Maps are presented. They are based on geometrical considerations and explore various possibilities regarding the norm and the direction of the adaptation vectors. The performance and convergence of each rule is evaluated by two criteria: topology preservation and quantization error.
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References
T. Kohonen. Self-organization of topologically correct feature maps. Biological Cybernetics, 43: 59-69, 1982.
T. Kohonen. Self-organization and Associaative Memory. Springer-Verlag, Berlin, 3rd edition, 1989.
A. Ahalt, A.K. Krishnamurthy, P. Chen and D.E. Melton. Competitive learning algorithms for vector quantization. Neural Networks, 3: 277-290, 1990.
T. Martinetz and K. Schulten. A neural gas network learns topologies. In T. Kohonen et al, editor, IEEE International Conference on Artificial Neural Networks, Espoo, Finland, volume 1, 397-407. Elsevier, 1991.
E.W. Dijkstra. A note on two problems in connection with graphs. Numerical Math., 1: 69-271, 1959.
H.U. Bauer and K.R. Pawelzik. Quantifying the neighborhood preservation of self-organizing maps. IEEE Transactions on Neural Networks, 3:570-579, 1992.
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© 2001 Springer-Verlag London Limited
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Lee, J.A., Donckers, N., Verleysen, M. (2001). Recursive learning rules for SOMs. In: Advances in Self-Organising Maps. Springer, London. https://doi.org/10.1007/978-1-4471-0715-6_10
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DOI: https://doi.org/10.1007/978-1-4471-0715-6_10
Publisher Name: Springer, London
Print ISBN: 978-1-85233-511-3
Online ISBN: 978-1-4471-0715-6
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