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An Unsupervised Learning Method that Produces Organized Representations from Real Information

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Artificial Neural Networks in Medicine and Biology

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

Abstract

The neural-network theories aim at two goals in medicine and biology: modeling of the neural structures and functions, and development of computational methods for the analysis of the experimental data. The Self-Organizing Map (SOM) was originally intended for the explanation of certain brain functions and organizations, but it has later been accepted as a new statistical analysis method to many fields of science and technology. At least 3700 scientific works on the SOM have been published. In its basic form, the SOM forms illustrative nonlinear projections of high-dimensional data manifolds, and these projections, usually produced on a two-dimensional display grid, help in the visualization and understanding of the relationships between complex data sets.

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Author information

Authors and Affiliations

  1. Neural Networks Research Centre, Helsinki University of Technology, 5400, FIN-02015 HUT, Finland

    Teuvo Kohonen

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  1. Teuvo Kohonen
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Editors and Affiliations

  1. Department of Philosophy, Göteborg University, Box 200, SE-405 30, Göteborg, Sweden

    Helge Malmgren BA, PhD, MD

  2. Department of Electrical Engineering, Linköping University, SE-585 83, Linköping, Sweden

    Magnus Borga MSc, PhD

  3. Department of Computer Science, University of Skövde, PO Box 408, SE-541 28, Skövde, Sweden

    Lars Niklasson BSc, MSc, PhD

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© 2000 Springer-Verlag London

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Kohonen, T. (2000). An Unsupervised Learning Method that Produces Organized Representations from Real Information. In: Malmgren, H., Borga, M., Niklasson, L. (eds) Artificial Neural Networks in Medicine and Biology. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0513-8_6

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  • DOI: https://doi.org/10.1007/978-1-4471-0513-8_6

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-289-1

  • Online ISBN: 978-1-4471-0513-8

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