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Neural Networks and Learning Machines
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.
"About this title" may belong to another edition of this title.
Book Description Softcover. Book Condition: New. Paperback. Book Condition: INTERNATIONAL EDITION, brand New, International/Global Edition, NOT LOOSE LEAF VERSION,NO SOLUTION MANUAL, NO CD, NO ACCESS CARD, Soft Cover/ Paper Back written in English, Different ISBN and Cover Image from US Edition; Sometimes, the title is different from US Edition, and the exercises and homework problem are in different orders or maybe completely different than the US edition, Please email us for confirmation. Some books may show some word such as Not for Sale or Restricted in US on the cover page. However, it is absolutely legal to use in USA. Bookseller Inventory # 9780131293762
Book Description Pearson Education (Us), 2009. Paperback. Book Condition: Brand New. 3rd international ed edition. 936 pages. In Stock. Bookseller Inventory # zk0131293761