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Overview
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.
This book presents the first comprehensive treatment of neural networks from an engineering perspective. Thorough, well-organized, and completely up-to-date, it examines all the important aspects of this emerging technology.
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.
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.
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/
Booknews
A textbook for a graduate course in engineering, computer science, and physics, but also perhaps useful for researchers in psychology and the neurosciences. Covers the nature of neural networks in largely qualitative terms, learning machines with and without a teacher, and nonlinear dynamical systems. The text is supported with examples, computer-oriented experiments, end-of- chapter problems, and two web sites. An instructor's manual is available. No date is mentioned for the first edition. Annotation c. by Book News, Inc., Portland, Or.