Neural Networks and Learning Machines (3rd Edition)

Neural Networks and Learning MachinesThird EditionSimon HaykinMcMaster University, CanadaThis 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.Simon O. Haykin is the author of 'Neural Networks and Learning Machines (3rd Edition)', published 2008 under ISBN 9780131471399 and ISBN 0131471392.