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Self-Organizing Maps
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.



Neural Computation and Self-Organizing Maps: An Introduction (Computation & Neural Systems Series)

Self-Organizing Map Formation: Foundations of Neural Computation (Computational Neuroscience)

Advances in Self-Organizing Maps: 8th International Workshop, WSOM 2011, Espoo, Finland, June 13-15,

Machine Learning: An Algorithmic Perspective (Chapman & Hall/Crc Machine Learning & Pattern Recognit

Networks, Crowds, and Markets: Reasoning About a Highly Connected World
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