Free Ebook Self-Organizing Maps, by Teuvo Kohonen
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Self-Organizing Maps, by Teuvo Kohonen
Free Ebook Self-Organizing Maps, by Teuvo Kohonen
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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: 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.
- Sales Rank: #2016680 in Books
- Published on: 2000-11-16
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x 1.19" w x 6.10" l, 1.00 pounds
- Binding: Paperback
- 521 pages
Review
"Rarely do books come along with an information density and value of content far above average. We now have another book in this category...a marvelous tool for finding just about any information that exists regarding self-organising maps. Rarely has any subject been provided with such an index of knowledge...Kohonen has created a masterpiece. I unhesitatingly recommend it." IEEE
From the Back Cover
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.
Most helpful customer reviews
0 of 0 people found the following review helpful.
Repetaive
By MR GEORGE S YOUNG
Somewhat repetitive, but clear. One can make wise use of SOMs from what is written here. A much shorter book would, however, have sufficed for the core content.
0 of 1 people found the following review helpful.
Five Stars
By Lappoon Rupert Tang
Book arrived perfectly.
13 of 15 people found the following review helpful.
A very nice 'handbook' of sorts for users of SOMs.
By doucette@spatial.maine.edu
The material is presented clearly and comprehensively from the unique perspective of the SOM originator himself. The inclusion of exhaustive references is particularly useful for the prospective researcher, but, at the risk of sounding ungrateful, I'm curious as to why paper titles were not included in the citations? Overall though, a very good reference.
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