02141cam a2200361 a 4500003000400000005001700004008004100021020001800062020001500080040000900095082001900104100002200123100001000145245005900155250001200214260006400226300001800290300001100308336002600319337002800345338002700373500004900400504006400449505066800513520034901181650002201530650000701552650002901559650003901588650002201627700004701649856008301696OSt20260630050647.0080731s2009 nyua b 001 0 eng  a9780131471399 a0131471392 cAKCL00a006.32bHAY-N31 aHaykin, Simon S.,1 d1931-10aNeural Networks and Learning Machines /cSimon Haykin. a3rd ed. aNoida :bPearson India Education Services Pvt. Ltd.,c2026. axxx, 906 p. : c24 cm. atextbtxt2rdacontent aunmediatedbn2rdamedia avolumebnc2rdacarrier aRev. ed of: Neural networks. 2nd ed., c1999. aIncludes bibliographical references (p. 847-887) and index. 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 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. 0aComputer Science  0aAI 0aArtificial Intelligence  0aNeural networks (Computer science) 0aAdaptive filters.1 aHaykin, Simon S.,d1931-tNeural networks.413Table of contents onlyuhttp://www.loc.gov/catdir/toc/ecip0824/2008034079.html