Монография. Third Edition With 129 Figures and 22 Tables.
- Berlin; Heidelberg; New York; Barcelona; Hong Kong; London; Milan; Paris; Singapore; Tokyo: Springer, 2001. (Springer series in information sciences; 30). ISBN 3-540-67921-9
Классическая монография от автора самоорганизующихся нейронных сетей.
Preface to the First Edition
The book we have at hand is the fourth monograph I wrote for Springer-Verlag. The previous one named "Self-Organization and Associative Memory" (Springer Series in Information Sciences, Volume 8) came out in 1984. Since then the self-organizing neural-network algorithms called SOM and LVQ have become very popular, as can be seen from the many works reviewed in Chap.
9. The new results obtained in the past ten years or so have warranted a new monograph. Over these years I have also answered lots of questions; they have influenced the contents of the present book.
I hope it would be of some interest and help to the readers if I now first very briefly describe the various phases that led to my present SOM research, and the reasons underlying each new step.
Preface to the Second Edition
The second, revised edition of this book came out sooner than originally planned. The main reason was that new important results had just been obtained.
The ASSOM (Adaptive-Subspace SOM) is a new architecture in which invariant-feature detectors emerge in an unsupervised learning process. Its basic principle was already introduced in the first edition, but the motivation and theoretical discussion in the second edition is more thorough and consequent. New material has been added to Sect. 5.9 and this section has been rewritten totally. Correspondingly, Sect. 1.4 that deals with adaptive-subspace classifiers in general and constitutes the prerequisite for the ASSOM principle has also been extended and rewritten totally.
Another new SOM development is the WEBSOM, a two-layer architecture intended for the organization of very large collections of full-text documents such as those found in the Internet and World Wide Web. This architecture was published after the first edition came out. The idea and results seemed to be so important that the new Sect. 7.8 has now been added to the second edition.
Preface to the Third Edition
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 complicated statistical analysis.
