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Self-Organizing Maps

  • Book
  • © 1995

Overview

Authors:
  1. Teuvo Kohonen

    1. Laboratory of Computer and Information Science, Helsinki University of Technology, Espoo 15, Finland

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Part of the book series: Springer Series in Information Sciences (SSINF, volume 30)

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Table of contents (10 chapters)

  1. Front Matter

    Pages I-XV
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  2. Mathematical Preliminaries

    • Teuvo Kohonen
    Pages 1-50

  3. Justification of Neural Modeling

    • Teuvo Kohonen
    Pages 51-75

  4. The Basic SOM

    • Teuvo Kohonen
    Pages 77-130

  5. Physiological Interpretation of SOM

    • Teuvo Kohonen
    Pages 131-141

  6. Variants of SOM

    • Teuvo Kohonen
    Pages 143-173

  7. Learning Vector Quantization

    • Teuvo Kohonen
    Pages 175-189

  8. Applications

    • Teuvo Kohonen
    Pages 191-213

  9. Hardware for SOM

    • Teuvo Kohonen
    Pages 215-230

  10. An Overview of SOM Literature

    • Teuvo Kohonen
    Pages 231-252

  11. Glossary of “Neural” Terms

    • Teuvo Kohonen
    Pages 253-281

  12. Back Matter

    Pages 283-364
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Keywords

About this book

The book we have at hand is the fourth monograph I wrote for Springer­ Verlag. The previous one named "Self-Organization and Associative Mem­ ory" (Springer Series in Information Sciences, Volume 8) came out in 1984. Since then the self-organizing neural-network algorithms called SOM and LVQ have become very popular, as can be seen from the many works re­ viewed in Chap. 9. The new results obtained in the past ten years or so have warranted a new monograph. Over these years I have also answered lots of questions; they have influenced the contents of the present book. I hope it would be of some interest and help to the readers if I now first very briefly describe the various phases that led to my present SOM research, and the reasons underlying each new step. I became interested in neural networks around 1960, but could not in­ terrupt my graduate studies in physics. After I was appointed Professor of Electronics in 1965, it still took some years to organize teaching at the uni­ versity. In 1968 - 69 I was on leave at the University of Washington, and D. Gabor had just published his convolution-correlation model of autoasso­ ciative memory. I noticed immediately that there was something not quite right about it: the capacity was very poor and the inherent noise and crosstalk were intolerable. In 1970 I therefore sugge~ted the auto associative correlation matrix memory model, at the same time as J.A. Anderson and K. Nakano.

Authors and Affiliations

  • Laboratory of Computer and Information Science, Helsinki University of Technology, Espoo 15, Finland

    Teuvo Kohonen

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