Neural Networks and Learning Machines

HAYKIN, Simon

ISBN 10: 0131471392 / ISBN 13: 9780131471399

Published by Pearson, New York, 2009

About this Item

xxx, 906 p. 24 cm. Numerous figures. Black and red text. Hardcover.

Seller Inventory # 125547

Bibliographic Details

• Title: Neural Networks and Learning Machines
• Publisher: Pearson, New York
• Publication Date: 2009
• Language: English
• ISBN 10: 0131471392
• ISBN 13: 9780131471399
• Binding: Hardcover
• Edition: Third Edition.
• Condition: Fine
• Seller catalogs:
  • Science

About this title

Synopsis

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.

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.