Self-Organizing Maps

Book Description

The Self-Organizing Map (SOM), with its variants, is the most popular artificial neural network algorithm in the unsupervised learning category. 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. A new area is organization of very large document collections. The SOM is also one of the most realistic models of the biological brain functions.
This new edition includes a survey of over 2000 contemporary studies to cover the newest results; the 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.

Customer Reviews:

I love this book........2000-03-11

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.

A very nice 'handbook' of sorts for users of SOMs........1999-08-05

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.

Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization

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A New Frontier in Computational Geometry
A fresh approach on topographic maps
A highly innovative but quite specialized angle!

Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization
Marc M. Van Hulle
Manufacturer: Wiley-Interscience
ProductGroup: Book
Binding: Hardcover

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ASIN: 0471345075

Book Description

A new perspective on topographic map formation and the advantages of information-based learning The study of topographic map formation provides us with important tools for both biological modeling and statistical data modeling. Faithful Representations and Topographic Maps offers a unified, systematic survey of this rapidly evolving field, focusing on current knowledge and available techniques for topographic map formation. The author presents a cutting-edge, information-based learning strategy for developing equiprobabilistic topographic maps—that is, maps in which all neurons have an equal probability to be active—clearly demonstrating how this approach yields faithful representations and how it can be successfully applied in such areas as density estimation, regression, clustering, and feature extraction. The book begins with the standard approach of distortion-based learning, discussing the commonly used Self-Organizing Map (SOM) algorithm and other algorithms, and pointing out their inadequacy for developing equiprobabilistic maps. It then examines the advantages of information-based learning techniques, and finally introduces a new algorithm for equiprobabilistic topographic map formation using neurons with kernel-based response characteristics. The complete learning algorithms and simulation details are given throughout, along with comparative performance analysis tables and extensive references. Faithful Representations and Topographic Maps is an excellent, eye-opening guide for neural network researchers, industrial scientists involved in data mining, and anyone interested in self-organization and topographic maps.

Customer Reviews:

A New Frontier in Computational Geometry.......2000-08-05

This is the first monograph devoted to an extremely important aspect of how nature organizes the sensory surfaces in the higher vertebrates. As this work points out, all sensory surfaces in mammals exist as two dimensional maps which project (or map) onto the (folded) two dimensional surface of the cerebral cortex. Previous authors have pointed out that in the case of the visual system, the retinal surface projects onto area V1 in the occipital cortex in a manner which approximates a "quasi-conformal" complex logarithmic function. Other authors have demonstrated that the cochlear basilar membrane also maps onto the auditory cortex in a similar quasi-conformal fashion. And most obviously, the mechano-receptors in our skin map onto the their corresponding cortical sensory areas in a manner which preserves local order of the projection. The important thing to note here is that this mapping of one 2-D surface onto another 2-D surface preserves the local orthogonality of the map coordinates (defn of quasi-conformal). (In the case of the retinal coordinates, they are the simple R, theta coordinates of the visual field and, in the case of the aural (auditory) map, the coordinates appear to be sound intensity (loudness) and frequency (pitch)). Van Hulle (Kohonen, et al.) describes, in vivid detail, how several "self-organizing" algorithms can make this mapping possible. THIS IS THE IMPORTANT POINT CONVEYED IN THIS MAGNIFICENT WORK. "The underlying INFORMATION coordinates of the data being processed by these 2-D onto 2-D mappings is what 'organizes' or defines the form of the mapping." The only point which the author does not address, is the global nature of these mappings. If we consider how the edges of these 2-D maps project onto eachother, there are three possible projections. One forms an ordinary torus and, unfolded looks like the raster scan of a TV set. This mapping is what has been assumed to be what would be followed in the neural projections of the retina onto area V1, but admits an indeterminacy in that there are two "normal" directions to approach the target surface. The second type of projection, when unfolded, resembles a Mobius strip (or Klein bottle for a closed surface). The importance of this type of projection is that there is only one way for neurons to project onto this surface which removes the ambiguity of the first mapping. (This surface is said to be non-orientable and of genus one in topological terms.) If this is in fact how nature chooses to wire the retino-topic projection, then she must also admit one singular point for each "patch" of map. (Consider what happens when you "squish" a Mobius strip made out of a paper strip between two flat surfaces. There is always a "fold".) About seven years ago, when the color distribution in the mammalian visual cortex was illucidated, it was found that the projection areas were made up of a "patch work quilt" of surfaces each with a singular point about which a set of color strips was splayed (like a peacock tail). Perhaps this is the necessary singular point for this type of retinotopic map. I would hope that the author, in his next book (or edition of this book) might address the global issues of these fascinating 2-D onto 2-D mappings, both natural and computational. (Just for closure, the third possible type of 2-D onto 2-D projection forms a hyperbolic surface whose physical significance is far from clear.)

If this review makes any sense to you, Please BUY THIS BOOK! I'm sure that you will be absolutely fascinated by it's content. However, be forwarned, it would be most helpful to have read Kohonen's "Self Organizing Maps", SV, 1995 before diving into this work. It is written in a "bottom up" fashion so a careful review of the Preface and Table of Contents will help in planning a reading strategy.

A fresh approach on topographic maps.......2000-03-30

The book presents a fresh approach on Topographic Maps, emphasizing ``equiprobabilistic'' topographic maps in which all representational units participate with the same probability in the representation. However, the text goes far beyond a monograph on this particular type of topographic maps and provides an excellent exposition of the topic of self-organizing map models in general, discussing their biological motivation and explaining in depth their connections with important statistical concepts such as vector quantization, non-parametric regression and density estimation. The practicioner will find detailed performance comparisons and psoudo code listings that tremendously facilitate an implementation of the described methods. The potential of the newly introduced equiprobabilistic topographic maps is amply demonstrated with detailed treatments of a broad range of application topics. I am convinced that this book marks an important contribution to the field of topographic map representations and that it has the potential to become a major reference for many years.

A highly innovative but quite specialized angle!.......2000-03-22

For those familiar with the technology and terminology of Kohonen's self-organizing maps, this book is a highly recommendable asset. The insights on the deficiencies of various previously developed techniques and how to improve them are brilliant. Although the matter is presented in a very bottom-up fashion, it is sometimes hard to keep focusing on the big picture while all the different aspects are in-depth explored. This makes the book sometimes hard to understand. Although it was probably never intended to be easily understandable, this is the reason why I didn't give it the full 5 stars.

Visual Explorations in Finance: with Self-Organizing Maps (Springer Finance)

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Essential SOM
Fascinating but the price isn't right...

Visual Explorations in Finance: with Self-Organizing Maps (Springer Finance)

Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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Self-Organizing Maps

ASIN: 3540762663

Book Description

Self-organizing maps (SOM) have proven to be of significant economic value in the areas of finance, economic and marketing applications. As a result, this area is rapidly becoming a non-academic technology. This book looks at near state-of-the-art SOM applications in the above areas, and is a multi-authored volume, edited by Guido Deboeck, a leading exponent in the use of computational methods in financial and economic forecasting, and by the originator of SOM, Teuvo Kohonen. The book contains chapters on applications of unsupervised neural networks using Kohonen's self-organizing map approach.

Customer Reviews:

Essential SOM.......2006-10-03

A classic for those who want rigorous mathematics applied to useful quantitative decisions. Despite being a collection of different chapters by different authors, do not worry. All of them, coordinated by Deboeck, make this book a "must" for those who, like me, are involved in the hard task of selecting Stocks, Bonds or Funds for the asset allocation process.

This is not the only book you should have in your shelves to know about this discipline. But it is the only one who will clearly show you the wide different applications of SOM in Financial Markets. Eminently practical (yes, it is true!), this is the perfect companion of the famous Kohonen's book.

Last, but not least, lots of examples very well documented and concisely explained. These can be easily implemented through the commercial software Mr Deboeck uses as tools.

Fascinating but the price isn't right..........2001-03-22

SOM are a kind of neural networks that are totally different from the feedforward multi-layered networks that everybody has heard about. The ideas behind SOM are remarkable intellectual stuff, first intellectual class stuff, and would certainly deserve to be more widely known. I found myself wondering how comes I rarely heard of Prof. Kohonen, and when is he going to win the Nobel prize... Prof. Kohonen and the development of SOM would make a good subject for the James Gleicks out there.

This book is in fact a collection of short articles about how SOM can be applied to financial visualisation and classification problems. Compilations of articles are a awkward genre, ranging from either the totally unitelligible or the totally trivial, and if you have graduate education in finance, you will find that most of the articles are pretty lame, if not downright amateurish, compared to standard academic finance literature; if you don't have grad education, I'm not sure whether you would want to buy this book in the first place, but there's nothing particularly hard about it. Art buffs may be attracted by the richly colored SOM maps (Vasarely-like) that come inside the book, and I could imagine somebody exposing these in an art gallery...

Anyway, after going through this book, one gets a decent idea of what can be done with SOM, so I would definitely say IT IS worth reading. I have to say I found it utterly fascinating, although I was disappointed with most of the articles.

Book Description

This book provides an overview of self-organizing map formation, including recent developments. Self-organizing maps form a branch of unsupervised learning, which is the study of what can be determined about the statistical properties of input data without explicit feedback from a teacher. The articles are drawn from the journal Neural Computation.

The book consists of five sections. The first section looks at attempts to model the organization of cortical maps and at the theory and applications of the related artificial neural network algorithms. The second section analyzes topographic maps and their formation via objective functions. The third section discusses cortical maps of stimulus features. The fourth section discusses self-organizing maps for unsupervised data analysis. The fifth section discusses extensions of self-organizing maps, including two surprising applications of mapping algorithms to standard computer science problems: combinatorial optimization and sorting.

Contributors
J. J. Atick, H. G. Barrow, H. U. Bauer, C. M. Bishop, H. J. Bray, J. Bruske, J. M. L. Budd, M. Budinich, V. Cherkassky, J. Cowan, R. Durbin, E. Erwin, G. J. Goodhill, T. Graepel, D. Grier, S. Kaski, T. Kohonen, H. Lappalainen, Z. Li, J. Lin, R. Linsker, S. P. Luttrell, D. J. C. MacKay, K. D. Miller, G. Mitchison, F. Mulier, K. Obermayer, C. Piepenbrock, H. Ritter, K. Schulten, T. J. Sejnowski, S. Smirnakis, G. Sommer, M. Svensen, R. Szeliski, A. Utsugi, C. K. I. Williams, L. Wiskott, L. Xu, A. Yuille, J. Zhang.

Adaptive Hierarchical Cluster Analysis By Self-Organizing Box Maps - ZIB Report, April 2000

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Adaptive Hierarchical Cluster Analysis By Self-Organizing Box Maps - ZIB Report, April 2000
Tobias; Peter Deufthard Galliat
Manufacturer: Konrad-Zuse-Zentrum
ProductGroup: Book
Binding: Paperback
ASIN: B000KGK9N2

Computational Intelligence in Economics and Finance: Volume II

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Computational Intelligence in Economics and Finance: Volume II

Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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Book Description

Computational intelligence (CI), as an alternative to statistical and econometric approaches, has been applied to a wide range of economics and finance problems in recent years, for example to price forecasting and market efficiency.

This book contains research ranging from applications in financial markets and business administration to various economics problems. Not only are empirical studies utilizing various CI algorithms presented, but so also are theoretical models based on computational methods. In addition to direct applications of computational intelligence, readers can also observe how these methods are combined with conventional analytical methods such as statistical and econometric models to yield preferred results.

Chen, Wang, and Kuo have grouped the 12 contributions following their introductory chapter into applications of fuzzy logic, neural networks (including self-organizing maps and support vector machines), and evolutionary computation. All chapters were selected either by invitation or based on a careful selection and extension of best papers from the International Workshop on Computational Intelligence in Economics and Finance in 2005. Overall, the book offers researchers an excellent overview of current advances and applications of computational intelligence techniques to economics and finance problems.

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Kohonen Maps

Manufacturer: Elsevier Science
ProductGroup: Book
Binding: Hardcover

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ASIN: 044450270X

Book 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.

Book Description

In the study of the computational structure of biological/robotic sensorimotor systems, distributed models have gained center stage in recent years, with a range of issues including self-organization, non-linear dynamics, field computing etc. This multidisciplinary research area is addressed here by a multidisciplinary team of contributors, who provide a balanced set of articulated presentations which include reviews, computational models, simulation studies, psychophysical, and neurophysiological experiments.

The book is divided into three parts, each characterized by a slightly different focus: in part I, the major theme concerns computational maps which typically model cortical areas, according to a view of the sensorimotor cortex as "geometric engine" and the site of "internal models" of external spaces. Part II also addresses problems of self-organization and field computing, but in a simpler computational architecture which, although lacking a specialized cortical machinery, can still behave in a very adaptive and surprising way by exploiting the interaction with the real world. Finally part III is focused on the motor control issues related to the physical properties of muscular actuators and the dynamic interactions with the world.

Book Description

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.

Customer Reviews:

SOM at its best.......2003-03-04

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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