Neural Networks and Learning Machines
A Comprehensive Foundation United States ed of 3rd revised ed
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Neural Networks and Learning Machines by S.S. Haykin
Book DescriptionFor graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.
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Book DetailsISBN: 9780131471399
(237mm x 188mm x 35mm)
Imprint: Prentice Hall
Publisher: Pearson Education (US)
Publish Date: 18-Nov-2008
Country of Publication: United States
Books By Author S.S. Haykin
Communication Systems, Hardback (March 2009)
Offers discussion on the theories and principles behind communications systems. This book emphasizes the statistical underpinnings of communication theory. It guides readers through topics ranging from pulse modulation and passband digital transmission to random processes and error-control coding. It includes treatment of digital communications.
Least-Mean-Square Adaptive Filters, Hardback (September 2003)
From elininating outside interference to separate data stream traveling together, filters are used for a range of applications in communications. The Least Mean Square (LMS) filter has established itself as the workhorse for the design of linear adaptive systems. This book deals with this topic.
Signals and Systems, Hardback (November 2002)
This title provides an integrated treatment of continuous-time and discrete-time forms of signals and systems intended to reflect their roles in engineering practice.
Unsupervised Adaptive Filtering, Hardback (April 2000)» View all books by S.S. Haykin
A complete, one-stop reference on the state of the art of unsupervised adaptive filtering While unsupervised adaptive filtering has its roots in the 1960s, more recent advances in signal processing, information theory, imaging, and remote sensing have made this a hot area for research in several diverse fields.
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