000			02445cam a2200433 a 4500
003			OSt
005			20260630050647.0
008			080731s2009 nyua b 001 0 eng
020			_a9780131471399
020			_a0131471392
040			_cAKCL
082	0	0	_a006.32 _bHAY-N3
100	1		_aHaykin, Simon S.,
100	1		_d1931-
245	1	0	_aNeural Networks and Learning Machines / _cSimon Haykin.
250			_a3rd ed.
260			_aNoida : _bPearson India Education Services Pvt. Ltd., _c2026.
300			_axxx, 906 p. :
300			_c24 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
500			_aRev. ed of: Neural networks. 2nd ed., c1999.
504			_aIncludes bibliographical references (p. 847-887) and index.
505			_aChapter 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
520			_aRefocused, 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		0	_aComputer Science
650		0	_aAI
650		0	_aArtificial Intelligence
650		0	_aNeural networks (Computer science)
650		0	_aAdaptive filters.
700	1		_aHaykin, Simon S., _d1931- _tNeural networks.
856	4	1	_3Table of contents only _uhttp://www.loc.gov/catdir/toc/ecip0824/2008034079.html
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_cBK
942			_2ddc _n0
999			_c44961 _d44961