Linked Data Explorer

"Mathematical preliminaries; Neural modeling; The basic SOM; Physiological interpretation of SOM; Variants of SOM; Learning vextor quantization; Applications; Software tools for SOM; Hardware for SOM; An overview of SOM literature; Glossary of "neural" terms; References; Index."
"The Self-Organizing Map (SOM) algorithm was introduced by the author in 1981. Its theory and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it. The most important practical applications are in exploratory data analysis, pattern recognition, speech analysis, robotics, industrial and medical diagnostics, instrumentation, and control, and literally hundreds of other tasks. In this monograph the mathematical preliminaries, background, basic ideas, and implications are expounded in a clear, well-organized form, accessible without prior expert knowledge. Still the contents are handled with theoretical rigor."
"Self-organizing maps deals with the most popular artificial neural-network algorithm of the unsupervised-learning category, the Self-Organizing Map (SOM). As this book is the main monograph on the subject, it discusses all the relevant aspects rangin 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."@en
"Self-organizing maps deals with the most popular artificial neural-network algorithm of the unsupervised-learning category, the Self-Organizing Map (SOM). As this book is the main monograph on the subject, it discusses all the relevant aspects rangin 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 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."
"The Self-Organizing Map (SOM) algorithm was introduced by the author in 1981. Its theory and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technolgies have already been based on it. The most important practical applications are in exploratory data analysis, pattern recognition, speech analysis, robotics, industrial and medical diagnostics, instrumentation, and control, and literally hundreds of other tasks. In this monograph the mathematical preliminaries, background, basic ideas, and implications are expounded in a clear, well-organized form, accessible without prior expert knowledge. Still the contents are handled with theoretical rigor."

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"Self-Organizing maps"
"Self-Organizing Maps"
"Self-organizing maps"@en
"Self-organizing maps"
"Self-organizing maps : with 22 tables"

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