Self-Organizing Maps

1. Front Matter

   Pages I-XX

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2. Mathematical Preliminaries

   • Teuvo Kohonen

   Pages 1-70

3. Neural Modeling

   • Teuvo Kohonen

   Pages 71-104

4. The Basic SOM

   • Teuvo Kohonen

   Pages 105-176

5. Physiological Interpretation of SOM

   • Teuvo Kohonen

   Pages 177-189

6. Variants of SOM

   • Teuvo Kohonen

   Pages 191-243

7. Learning Vector Quantization

   • Teuvo Kohonen

   Pages 245-261

8. Applications

   • Teuvo Kohonen

   Pages 263-310

9. Software Tools for SOM

   • Teuvo Kohonen

   Pages 311-328

10. Hardware for SOM

    • Teuvo Kohonen

    Pages 329-345

11. An Overview of SOM Literature

    • Teuvo Kohonen

    Pages 347-371

12. Back Matter

    Pages 373-501

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Since the second edition of this book came out in early 1997, the number of scientific papers published on the Self-Organizing Map (SOM) has increased from about 1500 to some 4000. Also, two special workshops dedicated to the SOM have been organized, not to mention numerous SOM sessions in neural­ network conferences. In view of this growing interest it was felt desirable to make extensive revisions to this book. They are of the following nature. Statistical pattern analysis has now been approached more carefully than earlier. A more detailed discussion of the eigenvectors and eigenvalues of symmetric matrices, which are the type usually encountered in statistics, has been included in Sect. 1.1.3: also, new probabilistic concepts, such as factor analysis, have been discussed in Sect. 1.3.1. A survey of projection methods (Sect. 1.3.2) has been added, in order to relate the SOM to classical paradigms. Vector Quantization is now discussed in one main section, and derivation of the pointdensity of the codebook vectors using the calculus of variations has been added, in order to familiarize the reader with this otherwise com­ plicated statistical analysis. It was also felt that the discussion of the neural-modeling philosophy should include a broader perspective of the main issues. A historical review in Sect. 2.2, and the general philosophy in Sects. 2.3, 2.5 and 2.14 are now expected to especially help newcomers to orient themselves better amongst the profusion of contemporary neural models.

• Adaptive and Learning Networks
• Adaptive und Lernende Netze
• CON_D044
• Cluster Analysis
• Klassifikator
• Klusteranalyse
• Lernen ohne Lehrer
• Neural Networks
• Neuronale Netze
• Selbstlernen
• Selbstorganisierende Karten
• pattern recognition
• self-organizing m

• Helsinki University of Technology Neural Networks Research Centre, HUT, Espoo, Finland

  Teuvo Kohonen

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