000			02102nam a2200241Ia 4500
003			NULRC
005			20250520102834.0
008			250520s9999 xx 000 0 und d
020			_a9780131471399
040			_cNULRC
050			_aQA 76.87 .H39 2009
100			_aHaykin, Simon. _eauthor
245		0	_aNeural networks and learning machines / _cSimon Haykin
250			_a3rd edition
260			_aNew York : _bPrentice Hall/Pearson, _cc2009
300			_axxx, 906 pages : _bcolor illustrations ; _c24 cm.
365			_bPHP11522.6
504			_aIncludes bibliographical references (pages 847-887) and index.
505			_aPreface-Introduction -- Chapter 1: Rosenblatt's Perceptron -- Chapter 2: Model Building through Regression-Chapter 3-The Least-Mean-Square-Algorithm -- Chapter 4 Multilayer Perceptrons-Chapter 5: Kernal Merhods 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 -Bibliography -- Index 889.
520			_aNeural Networks and Learning Machines, Third Edition is renowned for its thoroughness and readability. This well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. This is ideal for professional engineers and research scientists. 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.
650			_aNEURAL NETWORKS (COMPUTER SCIENCE)
942			_2lcc _cBK
999			_c16665 _d16665