Description
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.
Chapter
Chapter 2. Value maps: Finding value in markets that are expensive
pp.：
26 – 44
Chapter 3. Data mining and knowledge discovery with emergent Self-Organizing Feature Maps for multivariate time series
pp.：
44 – 58
Chapter 4. From aggregation operators to soft Learning Vector Quantization and clustering algorithms
pp.：
58 – 68
Chapter 5. Active learning in Self-Organizing Maps
pp.：
68 – 82
Chapter 6. Point prototype generation and classifier design
pp.：
82 – 108
Chapter 7. Self-Organizing Maps on non-Euclidean spaces
pp.：
108 – 122
Chapter 8. Self-Organising Maps for pattern recognition
pp.：
122 – 132
Chapter 9. Tree structured Self-Organizing Maps
pp.：
132 – 142
Chapter 10. Growing self-organizing networks — history, status quo, and perspectives
pp.：
142 – 156
Chapter 11. Kohonen Self-Organizing Map with quantized weights
pp.：
156 – 168
Chapter 12. On the optimization of Self-Organizing Maps by genetic algorithms
pp.：
168 – 182
Chapter 13. Self organization of a massive text document collection
pp.：
182 – 194
Chapter 14. Document classification with Self-Organizing Maps
pp.：
194 – 208
Chapter 15. Navigation in databases using Self-Organising Maps
pp.：
208 – 218
Chapter 16. A SOM-based sensing approach to robotic manipulation tasks
pp.：
218 – 230
Chapter 17. SOM-TSP: An approach to optimize surface component mounting on a printed circuit board
pp.：
230 – 242
Chapter 18. Self-Organising Maps in computer aided design of electronic circuits
pp.：
242 – 254
Chapter 19. Modeling self-organization in the visual cortex
pp.：
254 – 264
Chapter 20. A spatio-temporal memory based on SOMs with activity diffusion
pp.：
264 – 278
Chapter 21. Advances in modeling cortical maps
pp.：
278 – 290
Chapter 22. Topology preservation in Self-Organizing Maps
pp.：
290 – 304
Chapter 23. Second-order learning in Self-Organizing Maps
pp.：
304 – 314
Chapter 24. Energy functions for Self-Organizing Maps
pp.：
314 – 328
Chapter 25. LVQ and single trial EEG classification
pp.：
328 – 340
Chapter 26. Self-Organizing Map in categorization of voice qualities
pp.：
340 – 346
Chapter 27. Chemometric analyses with Self Organising Feature Maps: A worked example of the analysis of cosmetics using Raman spectroscopy
pp.：
346 – 360
Chapter 28. Self-Organizing Maps for content-based image database retrieval
pp.：
360 – 374
Chapter 29. Indexing audio documents by using latent semantic analysis and SOM
pp.：
374 – 386
Chapter 30. Self-Organizing Map in analysis of large-scale industrial systems
pp.：
386 – 400
Keyword index
pp.：
400 – 402
Kohonen Maps
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