Advanced Models of Neural Networks, Gerasimos G. Rigatos

Автор: Brian D. Ripley
Название: Pattern Recognition and Neural Networks
ISBN: 0521717701 ISBN-13(EAN): 9780521717700
Издательство: Cambridge Academ
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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: Bishop, Christopher M.
Название: Neural Networks for Pattern Recognition
ISBN: 0198538642 ISBN-13(EAN): 9780198538646
Издательство: Oxford Academ
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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.

Автор: Alessandra Lintas; Stefano Rovetta; Paul F.M.J. Ve
Название: Artificial Neural Networks and Machine Learning – ICANN 2017
ISBN: 3319686119 ISBN-13(EAN): 9783319686110
Издательство: Springer
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Описание: The two volume set, LNCS 10613 and 10614, constitutes the proceedings of then 26th International Conference on Artificial Neural Networks, ICANN 2017, held in Alghero, Italy, in September 2017. The 128 full papers included in this volume were carefully reviewed and selected from 270 submissions.

Автор: Eytan Domany; J. Leo van Hemmen; Klaus Schulten
Название: Models of Neural Networks III
ISBN: 0387943684 ISBN-13(EAN): 9780387943688
Издательство: Springer
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Описание: The introductory chapter, "Global Analysis of Recurrent Neural Net- works," by Andreas Herz presents an in-depth analysis of how to construct a Lyapunov function for various types of dynamics and neural coding.

Автор: Eytan Domany; J. Leo van Hemmen; Klaus Schulten
Название: Models of Neural Networks
ISBN: 0387943625 ISBN-13(EAN): 9780387943626
Издательство: Springer
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Описание: Provides an in-depth analysis of both paradigms starting with an introduction to the ideas used in the subsequent chapters. In this book, one finds a discussion of salient features such as coherent oscillations and their detection, associative binding and segregation, Hebbian learning, and sensory computations in the visual and olfactory cortex.

Автор: Huajin Tang; Kay Chen Tan; Zhang Yi
Название: Neural Networks: Computational Models and Applications
ISBN: 3540692258 ISBN-13(EAN): 9783540692256
Издательство: Springer
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Описание: Presents theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence.

Автор: Michael A. Arbib; Shun-ichi Amari
Название: Dynamic Interactions in Neural Networks: Models and Data
ISBN: 0387968938 ISBN-13(EAN): 9780387968933
Издательство: Springer
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Описание: The study of neural networks is enjoying a great renaissance, both in computational neuroscience - the development of information processing models of living brains - and in neural computing - the use of neurally inspired concepts in the construction of "intelligent" machines.