Neural Networks and Learning Machines: International Edition
Language: English
Published by Pearson, 2008
- Softcover
- Used

Seller: WorldofBooks, Goring-By-Sea, WS, United KingdomWorldofBooks
AbeBooks seller since March 16, 2007
Condition: Used - Very good
£ 233.92
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Add to basketItem description from seller
The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
Seller Inventory # GOR008098236
- Title
- Neural Networks and Learning Machines: International Edition
- Author
- Haykin, Simon O.
- Publisher
- Pearson
- Publication year
- 2008
- Condition
- Very Good
- Binding
- Paperback
- Language
- English
- ISBN 10
- 0131293761
- ISBN 13
- 9780131293762
- Edition
- 3rd Edition
- Item weight
- 1,293 grams
For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science.
Neural Networks and Learning Machines, Third Edition is 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. This is ideal for professional engineers and research scientists.
Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
Neural Networks and Learning Machines
Third Edition
Simon Haykin
McMaster University, Canada
This third edition of a classic book presents a comprehensive treatment of neural networks and learning machines. These two pillars that are closely related. The book has been revised extensively to provide an up-to-date treatment of a subject that is continually growing in importance. Distinctive features of the book include:
• On-line learning algorithms rooted in stochastic gradient descent; small-scale and large-scale learning problems.
• Kernel methods, including support vector machines, and the representer theorem.
• Information-theoretic learning models, including copulas, independent components analysis (ICA), coherent ICA, and information bottleneck.
• Stochastic dynamic programming, including approximate and neurodynamic procedures.
• Sequential state-estimation algorithms, including Kalman and particle filters.
• Recurrent neural networks trained using sequential-state estimation algorithms.
• Insightful computer-oriented experiments.
Just as importantly, the book is written in a readable style that is Simon Haykin’s hallmark.
"About the title" may belong to another edition of this title.
WorldofBooks
Goring-By-Sea, WS, United Kingdom
AbeBooks seller since March 16, 2007
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