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Self-organizing Map Formation

Self-organizing Map Formation
Author: Klaus Obermayer
Publisher: MIT Press
Total Pages: 472
Release: 2001
Genre: Neural computers
ISBN: 9780262650601

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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


Self-Organizing Maps

Self-Organizing Maps
Author: Teuvo Kohonen
Publisher: Springer Science & Business Media
Total Pages: 372
Release: 2012-12-06
Genre: Science
ISBN: 3642976107

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The book we have at hand is the fourth monograph I wrote for Springer Verlag. The previous one named "Self-Organization and Associative Mem ory" (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 re viewed 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. I became interested in neural networks around 1960, but could not in terrupt my graduate studies in physics. After I was appointed Professor of Electronics in 1965, it still took some years to organize teaching at the uni versity. In 1968 - 69 I was on leave at the University of Washington, and D. Gabor had just published his convolution-correlation model of autoasso ciative memory. I noticed immediately that there was something not quite right about it: the capacity was very poor and the inherent noise and crosstalk were intolerable. In 1970 I therefore sugge~ted the auto associative correlation matrix memory model, at the same time as J.A. Anderson and K. Nakano.


Self-Organizing Maps

Self-Organizing Maps
Author: Teuvo Kohonen
Publisher: Springer Science & Business Media
Total Pages: 514
Release: 2012-12-06
Genre: Science
ISBN: 3642569277

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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.


Self Organizing Maps

Self Organizing Maps
Author: Josphat Igadwa Mwasiagi
Publisher: BoD – Books on Demand
Total Pages: 717
Release: 2011-01-21
Genre: Computers
ISBN: 9533075465

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Kohonen Self Organizing Maps (SOM) has found application in practical all fields, especially those which tend to handle high dimensional data. SOM can be used for the clustering of genes in the medical field, the study of multi-media and web based contents and in the transportation industry, just to name a few. Apart from the aforementioned areas this book also covers the study of complex data found in meteorological and remotely sensed images acquired using satellite sensing. Data management and envelopment analysis has also been covered. The application of SOM in mechanical and manufacturing engineering forms another important area of this book. The final section of this book, addresses the design and application of novel variants of SOM algorithms.


Self-Organizing Neural Networks

Self-Organizing Neural Networks
Author: Udo Seiffert
Publisher: Physica
Total Pages: 289
Release: 2013-11-11
Genre: Computers
ISBN: 3790818100

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The Self-Organizing Map (SOM) is one of the most frequently used architectures for unsupervised artificial neural networks. Introduced by Teuvo Kohonen in the 1980s, SOMs have been developed as a very powerful method for visualization and unsupervised classification tasks by an active and innovative community of interna tional researchers. A number of extensions and modifications have been developed during the last two decades. The reason is surely not that the original algorithm was imperfect or inad equate. It is rather the universal applicability and easy handling of the SOM. Com pared to many other network paradigms, only a few parameters need to be arranged and thus also for a beginner the network leads to useful and reliable results. Never theless there is scope for improvements and sophisticated new developments as this book impressively demonstrates. The number of published applications utilizing the SOM appears to be unending. As the title of this book indicates, the reader will benefit from some of the latest the oretical developments and will become acquainted with a number of challenging real-world applications. Our aim in producing this book has been to provide an up to-date treatment of the field of self-organizing neural networks, which will be ac cessible to researchers, practitioners and graduated students from diverse disciplines in academics and industry. We are very grateful to the father of the SOMs, Professor Teuvo Kohonen for sup porting this book and contributing the first chapter.


Computational Intelligence Systems in Industrial Engineering

Computational Intelligence Systems in Industrial Engineering
Author: Cengiz Kahraman
Publisher: Springer Science & Business Media
Total Pages: 683
Release: 2012-11-05
Genre: Technology & Engineering
ISBN: 9491216775

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Industrial engineering is a branch of engineering dealing with the optimization of complex processes or systems. It is concerned with the development, improvement, implementation and evaluation of production and service systems. Computational Intelligence Systems find a wide application area in industrial engineering: neural networks in forecasting, fuzzy sets in capital budgeting, ant colony optimization in scheduling, Simulated Annealing in optimization, etc. This book will include most of the application areas of industrial engineering through these computational intelligence systems. In the literature, there is no book including many real and practical applications of Computational Intelligence Systems from the point of view of Industrial Engineering. Every chapter will include explanatory and didactic applications. It is aimed that the book will be a main source for MSc and PhD students.


Advances in Self-Organizing Maps

Advances in Self-Organizing Maps
Author: Pablo A. Estévez
Publisher: Springer Science & Business Media
Total Pages: 371
Release: 2012-12-14
Genre: Technology & Engineering
ISBN: 3642352308

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Self-organizing maps (SOMs) were developed by Teuvo Kohonen in the early eighties. Since then more than 10,000 works have been based on SOMs. SOMs are unsupervised neural networks useful for clustering and visualization purposes. Many SOM applications have been developed in engineering and science, and other fields. This book contains refereed papers presented at the 9th Workshop on Self-Organizing Maps (WSOM 2012) held at the Universidad de Chile, Santiago, Chile, on December 12-14, 2012. The workshop brought together researchers and practitioners in the field of self-organizing systems. Among the book chapters there are excellent examples of the use of SOMs in agriculture, computer science, data visualization, health systems, economics, engineering, social sciences, text and image analysis, and time series analysis. Other chapters present the latest theoretical work on SOMs as well as Learning Vector Quantization (LVQ) methods.


Kohonen Maps

Kohonen Maps
Author: E. Oja
Publisher: Elsevier
Total Pages: 401
Release: 1999-07-02
Genre: Computers
ISBN: 0080535291

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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. The 30 chapters of this book cover the current status of SOM theory, such as connections of SOM to clustering, classification, probabilistic models, and energy functions. Many applications of the SOM are given, with data mining and exploratory data analysis the central topic, applied to large databases of financial data, medical data, free-form text documents, digital images, speech, and process measurements. Biological models related to the SOM are also discussed.


Progress in Pattern Recognition, Image Analysis and Applications

Progress in Pattern Recognition, Image Analysis and Applications
Author: Alberto Sanfeliu
Publisher: Springer Science & Business Media
Total Pages: 720
Release: 2004-10-15
Genre: Computers
ISBN: 3540235272

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First of all, we want to congratulate two new research communities from M- ico and Brazil that have recently joined the Iberoamerican community and the International Association for Pattern Recognition. We believe that the series of congresses that started as the “Taller Iberoamericano de Reconocimiento de Patrones (TIARP)”, and later became the “Iberoamerican Congress on Pattern Recognition (CIARP)”, has contributed to these groupconsolidatione?orts. We hope that in the near future all the Iberoamerican countries will have their own groups and associations to promote our areas of interest; and that these congresses will serve as the forum for scienti?c research exchange, sharing of - pertise and new knowledge, and establishing contacts that improve cooperation between research groups in pattern recognition and related areas. CIARP 2004 (9th Iberoamerican Congress on Pattern Recognition) was the ninthinaseriesofpioneeringcongressesonpatternrecognitionintheIberoam- ican community. As in the previous year, CIARP 2004 also included worldwide participation. It took place in Puebla, Mexico. The aim of the congress was to promote and disseminate ongoing research and mathematical methods for pattern recognition, image analysis, and applications in such diverse areas as computer vision, robotics, industry, health, entertainment, space exploration, telecommunications, data mining, document analysis,and natural languagep- cessing and recognition, to name a few.