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Self-Organizing Maps [Paperback]

Teuvo Kohonen

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Book Description

Nov. 16 2000 3540679219 978-3540679219 3rd ed. 2001
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.

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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 --This text refers to an alternate Paperback edition.

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.

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Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com: 4.3 out of 5 stars  3 reviews
13 of 15 people found the following review helpful
4.0 out of 5 stars A very nice 'handbook' of sorts for users of SOMs. Aug. 4 1999
By doucette@spatial.maine.edu - Published on Amazon.com
Format:Paperback
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.
7 of 10 people found the following review helpful
5.0 out of 5 stars I love this book. March 10 2000
By A Customer - Published on Amazon.com
Format:Paperback
This is a wonderfully written, and excellent book. It assumes only minimal background knowledge but imparts a great deal of insight. I love the way that the author describes this area and the connections with deep and beautiful mathematics.
4.0 out of 5 stars Repetaive July 31 2013
By MR GEORGE S YOUNG - Published on Amazon.com
Format:Paperback|Verified Purchase
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.

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