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Initialising Self-Organising Maps

  • Emilio Corchado
  • Colin Fyfe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2690)

Abstract

We review a technique for creating Self-0rganising Maps (SOMs) in a Feature space which is nonlinearly related to the original data space. We show that convergence is remarkably fast for this method. By considering the linear feature space, we show that it is the interaction between the overcomplete basis in which learning takes place and the mixture of one-shot and incremental learning which comprises the method that gives the method its power. We illustrate the method on real and artificial data sets.

Keywords

Incremental Learning Initialisation Method Neighbourhood Function Kernel Space 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.

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References

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    Kohonen, T.: Self-Organising Maps. Springer, Heidelberg (1995)Google Scholar
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    MacDonald, D., Fyfe, C.: The kernel self-organising map. In: Howlett, R.J., Jain, L.C. (eds.) Fourth International Conference on Knowledge-based Intelligent Engineering Systems and Allied Technologies, KES (2000)Google Scholar
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    Michie, D., Spiegelhalter, D.J., Taylor, C.C. (eds.): Machine learning, neural and statistical classification, Ellis Horwood (1994)Google Scholar
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    Scholkopf, B., Smola, A., Muller, K.-R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10, 1299–1319 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Emilio Corchado
    • 1
  • Colin Fyfe
    • 1
  1. 1.School of Information and Communication TechnologiesThe University of Paisley

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