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Neural Networks and Learning Machines
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Neural Networks and Learning Machines Hardcover - 2007

by Simon Haykin


From the publisher

Rev. ed of: Neural networks. 2nd ed., 1999. Includes bibliographical references and index.

From the rear 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.

Details

  • Title Neural Networks and Learning Machines
  • Author Simon Haykin
  • Binding Hardcover
  • Edition INTERNATIONAL ED
  • Pages 936
  • Volumes 1
  • Language ENG
  • Publisher Pearson, China
  • Date May 1, 2007
  • Illustrated Yes
  • Features Bibliography, Glossary, Illustrated, Index, Table of Contents
  • ISBN 9780131471399 / 0131471392
  • Weight 3.6 lbs (1.63 kg)
  • Dimensions 9.2 x 7.2 x 2.1 in (23.37 x 18.29 x 5.33 cm)
  • Library of Congress subjects Neural networks (Computer science), Adaptive filters
  • Library of Congress Catalog Number 2008034079
  • Dewey Decimal Code 006.32
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