02445cam a2200433 a 4500 OSt 20260630050647.0 080731s2009 nyua b 001 0 eng 9780131471399 0131471392 AKCL 006.32 HAY-N3 Haykin, Simon S., 1931- Neural Networks and Learning Machines / Simon Haykin. 3rd ed. Noida : Pearson India Education Services Pvt. Ltd., 2026. xxx, 906 p. : 24 cm. text txt rdacontent unmediated n rdamedia volume nc rdacarrier Rev. ed of: Neural networks. 2nd ed., c1999. Includes bibliographical references (p. 847-887) and index. 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 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. Computer Science AI Artificial Intelligence Neural networks (Computer science) Adaptive filters. Haykin, Simon S., 1931- Neural networks. Table of contents only http://www.loc.gov/catdir/toc/ecip0824/2008034079.html 7 cbc orignew 1 ecip 20 y-gencatlg BK ddc 0 44961 44961 0 0 ddc 0 0 AKCL AKCL 2026-06-30 1030.00 0 006.32 SIN-N3 69317 2026-06-30 05:07:16 1030.00 2026-06-30 BK