Haykin, Simon S. creator 1931- creator text bibliography nyu Noida Pearson India Education Services Pvt. Ltd. 2026 2009 3rd ed. monographic eng

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xxx, 906 p. : 24 cm. Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently. Chapter 1 Rosenblatt's Perceptron Chapter 2 Model Building through Regression Chapter 3 The Least-Mean-Square Algorithm Chapter 4 Multilayer Perceptrons Chapter 5 Kernel Methods and Radial-Basis Function Networks Chapter 6 Support Vector Machines Chapter 7 Regularization Theory Chapter 8 Principal-Components Analysis Chapter 9 Self-Organizing Maps Chapter 10 Information-Theoretic Learning Models Chapter 11 Stochastic Methods Rooted in Statistical Mechanics Chapter 12 Dynamic Programming Chapter 13 Neurodynamics Chapter 14 Bayseian Filtering for State Estimation of Dynamic Systems Chapter 15 Dynamically Driven Recurrent Networks Simon Haykin. Rev. ed of: Neural networks. 2nd ed., c1999. Includes bibliographical references (p. 847-887) and index. Computer Science AI Artificial Intelligence Neural networks (Computer science) Adaptive filters 006.32 HAY-N3 Haykin, Simon S., 1931- 9780131471399 0131471392 http://www.loc.gov/catdir/toc/ecip0824/2008034079.html http://www.loc.gov/catdir/toc/ecip0824/2008034079.html 080731 20260630050647.0