Neural Networks and Learning Machines (3rd Edition)
Neural Networks and Learning MachinesThird EditionSimon HaykinMcMaster 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.