Description:
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.
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