Self-Organizing Maps

The Self-organizing Map (som), With Its Variants, Is The Most Popular Artificial Neural Network Algorithm In The Unsupervised Learning Category. About 4000 Research Articles On It Have Appeared In The Open Literature, And Many Industrial Projects Use The Som As A Tool For Solving Hard Real-world Problems. Many Fields Of Science Have Adopted The Som As A Standard Analytical Tool: In Statistics, Signal Processing, Control Theory, Financial Analyses, Experimental Physics, Chemistry And Medicine. The Som Solves Difficult High-dimensional And Nonlinear Problems Such As Feature Extraction And Classification Of Images And Acoustic Patterns, Adaptive Control Of Robots, And Equalization, Demodulation, And Error-tolerant Transmission Of Signals In Telecommunications. A New Area Is Organization Of Very Large Document Collections. Last But Not Least, It May Be Mentioned That The Som Is One Of The Most Realistic Models Of The Biological Brain Function. This New Edition Includes A Survey Of Over 2000 Contemporary Studies To Cover The Newest Results; Case Examples Were Provided With Detailed Formulae, Illustrations, And Tables; A New Chapter On Software Tools For Som Was Written, Other Chapters Were Extended Or Reorganized. Mathematical Preliminaries -- Neural Modeling -- The Basic Som -- Physiological Interpretation Of Som -- Variants Of Som -- Learning Vector Quantization -- Applications -- Software Tools For Som -- Hardware For Som -- An Overview Of Som Literature -- Glossary Of Neural Terms -- References. Teuvo Kohonen. Includes Bibliographical References (p. [403]-486) And Index. 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 point density 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. The Self-Organizing Map (SOM), with its variants, is the most popular artificial neural network algorithm in the unsupervised learning category. About 4000 research articles on it have appeared in the open literature, and many industrial projects use the SOM as a tool for solving hard real world problems. Many fields of science have adopted the SOM as a standard analytical statistics, signal processing, control theory, financial analyses, experimental physics, chemistry and medicine. This new edition includes a survey of over 2000 contemporary studies to cover the newest results. Case examples are provided with detailed formulae, illustrations, and tables. Further, a new chapter on software tools for SOM has been included whilst other chapters have been extended and reorganised. The Self-Organizing Map (SOM) algorithm was introduced by the author in 1981. Its theory and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technolgies have already been based on it. The most important practical applications are in exploratory data analysis, pattern recognition, speech analysis, robotics, industrial and medical diagnostics, instrumentation, and control, and literally hundreds of other tasks. In this monograph the mathematical preliminaries, background, basic ideas, and implications are expounded in a clear, well-organized form, accessible without prior expert knowledge. Still the contents are handled with theoretical rigor. Front Matter....Pages I-XX Mathematical Preliminaries....Pages 1-70 Neural Modeling....Pages 71-104 The Basic SOM....Pages 105-176 Physiological Interpretation of SOM....Pages 177-189 Variants of SOM....Pages 191-243 Learning Vector Quantization....Pages 245-261 Applications....Pages 263-310 Software Tools for SOM....Pages 311-328 Hardware for SOM....Pages 329-345 An Overview of SOM Literature....Pages 347-371 Back Matter....Pages 373-501 Self-Organizing Maps (SOM), is the most popular artificial neural network algorithm for solving hard real-world problems. SOM is adapted as a standard analytical tool: statistics, signal processing, control theory, financial analysis and model of the biological brain function