Wavelet Neural Networks, Alexandridis Antonis K.

Автор: Brian D. Ripley
Название: Pattern Recognition and Neural Networks
ISBN: 0521717701 ISBN-13(EAN): 9780521717700
Издательство: Cambridge Academ
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Цена: 51750.00 T
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Описание: This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. Valuable advice is included on both theory and applications, while case studies based on real data sets help readers develop their understanding. All data sets are available from www.stats.ox.ac.uk/~ripley/PRbook/

Автор: Gurney, Kevin
Название: Introduction to neural networks
ISBN: 1857285034 ISBN-13(EAN): 9781857285031
Издательство: Taylor&Francis
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Цена: 71450.00 T
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Описание: Although mathematical ideas underpin the study of neural networks, this book presents the fundamentals without the full mathematical apparatus. It tackles various aspects of the field, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; and gradient descent methods.

Автор: Bishop, Christopher M.
Название: Neural Networks for Pattern Recognition
ISBN: 0198538642 ISBN-13(EAN): 9780198538646
Издательство: Oxford Academ
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Цена: 92930.00 T
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Описание: This book is the first to provide a comprehensive account of neural networks from a statistical perspective. Its emphasis is on pattern recognition, which currently represents the area of greatest applicability for neural networks. By focusing on pattern recognition, the book provides a much more extensive treatment of many topics than is available in earlier books.

Название: A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
ISBN: 1849968675 ISBN-13(EAN): 9781849968676
Издательство: Springer
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Цена: 156720.00 T
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Описание: How does a machine learn a new concept on the basis of examples? This second edition takes account of important new developments in the field. It also deals extensively with the theory of learning control systems, now comparably mature to learning of neural networks.