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Self-Organising Neural Maps and their Applications

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Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

Abstract

This tutorial paper is an introduction to Kohonen’s self-organising neural maps. It discusses their operating principles, relationship to classical pattern recognition and outlines various applications.

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References

  1. Kohonen T. Self-Organization and Associative Memory. Springer Verlag, Heidelberg, 1984

    MATH  Google Scholar 

  2. Kohonen T. Biol Cyb 1982; 44: 135–140

    Article  MathSciNet  MATH  Google Scholar 

  3. Cotterall M and Fort J C. Ann Inst Henri Poincare 1987; 23: 1–20

    Google Scholar 

  4. Tattersall G D, Linford P W and Linggard R. Brit Telecom Tech J 1988; 6: 140–163

    Google Scholar 

  5. Duda R O and Hart P E. Pattern Classification and Scene Analysis. Wiley, New York, 1973

    MATH  Google Scholar 

  6. Oja E. Int J Neural Systems 1989; 1: 61–68

    Article  MathSciNet  Google Scholar 

  7. Grossberg S. Cognitive Science 1987; 11: 23–63

    Article  Google Scholar 

  8. Hecht-Nielson R. Counterpropagation networks. In Proc 1st int conf on neural networks. IEEE San Diego, 1988, pp 19–32

    Google Scholar 

  9. Naylor J and Feng Y. Analysis of a neural network algorithm for the vector quantization of speech parameters. In Proc 1st ann INNS meeting. Pergamon Press New York, 1988, p 310

    Google Scholar 

  10. Nasrabadi N M and Feng Y. Vector quantization of images based upon the Kohonen self-organising feature maps. In Proc 1st int conf on neural networks. IEEE San Diego, 1988, pp 1101–1108

    Google Scholar 

  11. Kohonen T. Speech recognition based on topology-preserving neural maps. In Aleksander I (ed) Neural computing architectures. North Oxford Academic London, 1989, pp 26–40

    Google Scholar 

  12. Tattersall G D. Neural map applications. In Aleksander I (ed) Neural computing architectures. North Oxford Academic London, 1989, pp 41–73

    Google Scholar 

  13. Johnson M J and Allinson N M. Digital realisation of self-organising maps. In Touretzky D S (ed) Advances in neural information processing systems. Morgan Kaufmann San Mateo, 1989, pp 728–738

    Google Scholar 

  14. Luckman A J and Allinson N M. Proc SPIE 1990; 1197: 68–75

    Google Scholar 

  15. Angeniol B, de la Croix Vaubois G and Texier J-Y. Neural Networks 1988; 1: 289–293

    Article  Google Scholar 

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© 1992 Springer-Verlag London Limited

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Allinson, N.M. (1992). Self-Organising Neural Maps and their Applications. In: Taylor, J.G., Mannion, C.L.T. (eds) Theory and Applications of Neural Networks. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-1833-6_4

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  • DOI: https://doi.org/10.1007/978-1-4471-1833-6_4

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-19650-1

  • Online ISBN: 978-1-4471-1833-6

  • eBook Packages: Springer Book Archive

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