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Neural networks and statistical learning

Author: K -L Du; M N S Swamy
Publisher: London, United Kingdom : Springer, 2019.
Edition/Format:   eBook : Document : English : Second edition
Summary:
This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents  Read more...
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Details

Genre/Form: Electronic books
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: K -L Du; M N S Swamy
ISBN: 9781447174523 1447174526
OCLC Number: 1120105268
Description: 1 online resource (xxx, 988 pages) : illustrations (some color)
Contents: Introduction --
Fundamentals of Machine Learning --
Perceptrons --
Multilayer perceptrons: architecture and error backpropagation --
Multilayer perceptrons: other learing techniques --
Hopfield networks, simulated annealing and chaotic neural networks --
Associative memory networks --
Clustering I: Basic clustering models and algorithms --
Clustering II: topics in clustering --
Radial basis function networks --
Recurrent neural networks --
Principal component analysis --
Nonnegative matrix factorization and compressed sensing --
Independent component analysis --
Discriminant analysis --
Support vector machines --
Other kernel methods --
Reinforcement learning --
Probabilistic and Bayesian networks --
Combining multiple learners: data fusion and emsemble learning --
Introduction of fuzzy sets and logic --
Neurofuzzy systems --
Neural circuits --
Pattern recognition for biometrics and bioinformatics --
Data mining.
Responsibility: Ke-Lin Du, M. N. S. Swamy.

Abstract:

Inclusive coverage of all the essential neural network applications in a statistical learning framework makes this a baseline text for students and researchers, with 25 chapters on all the major  Read more...

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