Ebook: Self-Organizing Maps

Self-Organizing Maps deals with the most popular artificial neural-network algorithm of the unsupervised-learning category, viz. the Self-Organizing Map (SOM). As this book is the main monograph on the subject, it discusses all the relevant aspects ranging from the history, motivation, fundamentals, theory, variants, advances, and applications, to the hardware of SOMs. An extensive literature survey of over 2000 contemporary studies is included. Thus, answers to the most frequently asked questions relating to this topic can be found in this volume. The subject is presented in a didactive manner and only a general theoretical background is required. The reader will be guided by the many case studies to the very frontier of modern research in this area.

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Self-Organizing Maps deals with the most popular artificial neural-network algorithm of the unsupervised-learning category, viz. the Self-Organizing Map (SOM). As this book is the main monograph on the subject, it discusses all the relevant aspects ranging from the history, motivation, fundamentals, theory, variants, advances, and applications, to the hardware of SOMs. An extensive literature survey of over 2000 contemporary studies is included. Thus, answers to the most frequently asked questions relating to this topic can be found in this volume. The subject is presented in a didactive manner and only a general theoretical background is required. The reader will be guided by the many case studies to the very frontier of modern research in this area.
Content:
Front Matter....Pages I-XVII
Mathematical Preliminaries....Pages 1-58
Justification of Neural Modeling....Pages 59-83
The Basic SOM....Pages 85-144
Physiological Interpretation of SOM....Pages 145-155
Variants of SOM....Pages 157-201
Learning Vector Quantization....Pages 203-217
Applications....Pages 219-260
Hardware for SOM....Pages 261-276
An Overview of SOM Literature....Pages 277-301
Glossary of “Neural” Terms....Pages 303-331
Back Matter....Pages 333-428

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Self-Organizing Maps deals with the most popular artificial neural-network algorithm of the unsupervised-learning category, viz. the Self-Organizing Map (SOM). As this book is the main monograph on the subject, it discusses all the relevant aspects ranging from the history, motivation, fundamentals, theory, variants, advances, and applications, to the hardware of SOMs. An extensive literature survey of over 2000 contemporary studies is included. Thus, answers to the most frequently asked questions relating to this topic can be found in this volume. The subject is presented in a didactive manner and only a general theoretical background is required. The reader will be guided by the many case studies to the very frontier of modern research in this area.
Content:
Front Matter....Pages I-XVII
Mathematical Preliminaries....Pages 1-58
Justification of Neural Modeling....Pages 59-83
The Basic SOM....Pages 85-144
Physiological Interpretation of SOM....Pages 145-155
Variants of SOM....Pages 157-201
Learning Vector Quantization....Pages 203-217
Applications....Pages 219-260
Hardware for SOM....Pages 261-276
An Overview of SOM Literature....Pages 277-301
Glossary of “Neural” Terms....Pages 303-331
Back Matter....Pages 333-428
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