Elsevier S & T

Kohonen Maps

Oja, E.; Kaski, S.

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Table of contents
  • Cover
  • Table of Contentsvii
  • Preface: Kohonen Mapsv
  • Chapter 1. Analyzing and representing multidimensional quantitative and qualitative data: Demographi1
  • Chapter 2. Value maps: Finding value in markets that are expensive15
  • Chapter 3. Data mining and knowledge discovery with emergent Self-Organizing Feature Maps for multiv33
  • Chapter 4. From aggregation operators to soft Learning Vector Quantization and clustering algorithms47
  • Chapter 5. Active learning in Self-Organizing Maps57
  • Chapter 6. Point prototype generation and classifier design71
  • Chapter 7. Self-Organizing Maps on non-Euclidean spaces97
  • Chapter 8. Self-Organising Maps for pattern recognition111
  • Chapter 9. Tree structured Self-Organizing Maps121
  • Chapter 10. Growing self-organizing networks „ history, status quo, and perspectives131
  • Chapter 11. Kohonen Self-Organizing Map with quantized weights145
  • Chapter 12. On the optimization of Self-Organizing Maps by genetic algorithms157
  • Chapter 13. Self organization of a massive text document collection171
  • Chapter 14. Document classification with Self-Organizing Maps183
  • Chapter 15. Navigation in databases using Self-Organising Maps197
  • Chapter 16. A SOM-based sensing approach to robotic manipulation tasks207
  • Chapter 17. SOM-TSP: An approach to optimize surface component mounting on a printed circuit board219
  • Chapter 18. Self-Organising Maps in computer aided design of electronic circuits231
  • Chapter 19. Modeling self-organization in the visual cortex243
  • Chapter 20. A spatio-temporal memory based on SOMs with activity diffusion253
  • Chapter 21. Advances in modeling cortical maps267
  • Chapter 22. Topology preservation in Self-Organizing Maps279
  • Chapter 23. Second-order learning in Self-Organizing Maps293
  • Chapter 24. Energy functions for Self-Organizing Maps303
  • Chapter 25. LVQ and single trial EEG classification317
  • Chapter 26. Self-Organizing Map in categorization of voice qualities329
  • Chapter 27. Chemometric analyses with Self Organising Feature Maps: A worked example of the analysis335
  • Chapter 28. Self-Organizing Maps for content-based image database retrieval349
  • Chapter 29. Indexing audio documents by using latent semantic analysis and SOM363
  • Chapter 30. Self-Organizing Map in analysis of large-scale industrial systems375
  • Keyword index389
Book details
  • Vendor Elsevier S & T
  • SKU 9780444502704
  • ISBN-13 9780080535296
  • Author Oja, E.; Kaski, S.
  • Category Computers
  • Subject Neural Networks

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The Self-Organizing Map, or Kohonen Map, is one of the most widely used neural network algorithms, with thousands of applications covered in the literature. It was one of the strong underlying factors in the popularity of neural networks starting in the early 80's. Currently this method has been included in a large number of commercial and public domain software packages. In this book, top experts on the SOM method take a look at the state of the art and the future of this computing paradigm.


The 30 chapters of this book cover the current status of SOM theory, such as connections of SOM to clustering, classification, probabilistic models, and energy functions. Many applications of the SOM are given, with data mining and exploratory data analysis the central topic, applied to large databases of financial data, medical data, free-form text documents, digital images, speech, and process measurements. Biological models related to the SOM are also discussed.