Neural Networks and Learning Machines - 3rd Edition by Simon Haykin (Hardcover)

Number of Pages: 906
Genre: Computers + Internet
Sub-Genre: Neural Networks
Format: Hardcover
Publisher: Pearson
Age Range: Adult
Author: Simon Haykin
Featured book lists: Textbooks
Language: English

Book Synopsis

Fluid and authoritative, this well-organized book represents the first comprehensive treatment of neural networks and learning machines from an engineering perspective, providing extensive, state-of-the-art coverage that will expose readers to the myriad facets of neural networks and help them appreciate the technology's origin, capabilities, and potential applications. KEY TOPICS: Examines all the important aspects of this emerging technology, covering the learning process, back propogation, radial basis functions, recurrent networks, self-organizing systems, modular networks, temporal processing, neurodynamics, and VLSI implementation. Integrates computer experiments throughout to demonstrate how neural networks are designed and perform in practice. Chapter objectives, problems, worked examples, a bibliography, photographs, illustrations, and a thorough glossary all reinforce concepts throughout. New chapters delve into such areas as support vector machines, and reinforcement learning/neurodynamic programming, Rosenblatt's Perceptron, Least-Mean-Square Algorithm, Regularization Theory, Kernel Methods and Radial-Basis function networks (RBF), and Bayseian Filtering for State Estimation of Dynamic Systems. An entire chapter of case studies illustrates the real-life, practical applications of neural networks. A highly detailed bibliography is included for easy reference. MARKET: 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/

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.