This compilation contains new theoretical work as well as up-to-date applications contributed by some of the world leaders of current SOM research. It is a compact and vivid collection of almost all aspects of current research on Self-Organizing Maps, ranging from theoretical work, several technical and non-technical applications, numerical and implementation details on sequential and parallel hardware to self-organisation with spiking neurons. An overture, given by Teuvo Kohonen, builds a bridge from the development of the fundamentals to the many extensions, modifications and applications which have made this neural network architecture so successful.
Self-Organizing Neural Networks
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1 of 1 people found the following review helpful:
5.0 out of 5 stars
SOM at its best,
This review is from: Self-Organizing Neural Networks: Recent Advances and Applications (Studies in Fuzziness and Soft Computing) (Hardcover)
This book seems to be the most recent and comprehensive survey of SOM applications which are described in detail. Really good technical chapters, could have another one or two on financial business. I have never read such a deep chapter on SOM quality measurements. Overall, good complement to Kohonen's bible.
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Inside This Book
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First Sentence:
The Self-Organizing Map (SOM) [1], [2], |3] is a computational mapping principle that forms an ordered nonlinear projection of high-dimensional input data items onto a low-dimensional, usually 2D regular grid. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
hypermap architecture, third learning phase, facto publications, cochlear model, competitive neurons, topographic function, parallel mapping, initialisation weights, probability density model, topology preservation, spiking neurons, neighborhood width, data manifold, hyperspectral images, competitive learning rule, detector neuron, two phase learning, motion detection system, firing rate patterns, biological neural systems, coding interval, global winner, magnification control, preserving behavior, support vector learning Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Self Organizing Maps, Dow Jones, Biological Cybernetics, John Wiley, Friedhelm Schwenker, Genetic Algorithms, International Journal of Computer Vision, Lecture Notes, Random Walk Hypothesis, Service Center, Elsevier Science Publishers, Helsinki University of Technology, Los Alamitos, Parallel Computing, Prentice Hall, Proceedings of International Conference, Test Set Null Hyp, The Netherlands, Van Hulle New!
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Front Cover | Table of Contents | First Pages | Back Cover | Surprise Me!
The Self-Organizing Map (SOM) [1], [2], |3] is a computational mapping principle that forms an ordered nonlinear projection of high-dimensional input data items onto a low-dimensional, usually 2D regular grid. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
hypermap architecture, third learning phase, facto publications, cochlear model, competitive neurons, topographic function, parallel mapping, initialisation weights, probability density model, topology preservation, spiking neurons, neighborhood width, data manifold, hyperspectral images, competitive learning rule, detector neuron, two phase learning, motion detection system, firing rate patterns, biological neural systems, coding interval, global winner, magnification control, preserving behavior, support vector learning Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Self Organizing Maps, Dow Jones, Biological Cybernetics, John Wiley, Friedhelm Schwenker, Genetic Algorithms, International Journal of Computer Vision, Lecture Notes, Random Walk Hypothesis, Service Center, Elsevier Science Publishers, Helsinki University of Technology, Los Alamitos, Parallel Computing, Prentice Hall, Proceedings of International Conference, Test Set Null Hyp, The Netherlands, Van Hulle New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Back Cover | Surprise Me!
Citations (learn more)
This book cites 52
books:
See all 52 books this book cites
3
books
cite this book:
- Self-Organizing Maps by Teuvo Kohonen on 18 pages
- Kohonen Maps by E. Oja on 8 pages
- Neural Networks Hb (UNICOM - Information & Communications Technology) by J G Taylor on page 72, page 163, and page 182
- Introduction To The Theory Of Neural Computation, Volume I (Santa Fe Institute Series) by John A. Hertz on page 141, and page 242
- Machine Learning, Neural and Statistical Classification (Ellis Horwood Series in Artificial Intelligence) by D. Michie on page 182, and page 243
See all 52 books this book cites
- Biologically Inspired Approaches to Advanced Information Technology: First International Workshop, BioADIT 2004, Lausanne, Switzerland, January 29-30, ... Papers (Lecture Notes in Computer Science) on page 439
- Bioinformatics Using Computational Intelligence Paradigms (Studies in Fuzziness and Soft Computing) by Udo Seiffert on page 4
- Biologically Inspired Approaches to Advanced Information Technology: Second International Workshop, BioADIT 2006, Osaka, Japan 26-27, 2006, ... Computer Science and General Issues) by Auke Jan Ijspeert on page 157
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