Pattern Recognition by Self-Organizing Neural Networks (Bradford Books)
Pattern Recognition by Self-Organizing Neural Networks presents the most recent advances in an area of research that is becoming vitally important in the fields of cognitive science, neuroscience, artificial intelligence, and neural networks in general. Get and download textbook Pattern Recognition by Self-Organizing Neural Networks (Bradford Books) for free
Categories: Pattern recognition systems, Neural networks (Computer science). Contributors: Gail A. Carpenter - Editor. Format: Hardcover
The 19 articles take up developments in competitive learning and computational maps, adaptive resonance theory, and specialized architectures and biological connections.Introductory survey articles provide a framework for understanding the many models involved in various approaches to studying neural networks. These are followed in Part 2 by articles that form the foundation for models of competitive learning and computational mapping, and recent articles by Kohonen, applying them to problems in Pattern Recognition by Self-Organizing Neural Networks new edition
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Categories: Pattern recognition systems, Neural networks (Computer science). Contributors: Gail A. Carpenter - Editor. Format: Hardcover
Categories: Pattern recognition systems, Neural networks (Computer science). Contributors: Gail A. Carpenter - Editor. Format: Hardcover
Pattern Recognition by Self-Organizing Neural Networks presents the most recent advances in an area of research that is becoming vitally important in the fields of cognitive science, neuroscience, artificial intelligence, and neural networks in general.
A Bradford Book 1991-04-18. Hardcover. New. New - excellent clean condition has a mark on the edge of the book clean pages Soft bound Publisher: The MIT Press
Pattern Recognition by Self-Organizing Neural Networks Textbook
The 19 articles take up developments in competitive learning and computational maps, adaptive resonance theory, and specialized architectures and biological connections
These are followed in Part 2 by articles that form the foundation for models of competitive learning and computational mapping, and recent articles by Kohonen, applying them to problems in
