Neural networks and learning machines Simon Haykin
Material type: TextPublication details: Noida Pearson 2016Edition: 3/eDescription: 909p. ill., 24cmISBN:- 9789332570313
- 004.89 HAY
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004.7 TAN Computer Networks | 004.8 NIL Artificial intelligence a new synthesis | 004.8 RIC Artificial intelligence | 004.89 HAY Neural networks and learning machines | 004.896 ZEI CAD/CAM theory and practice / | 004.2MUR Computer architecture and organization: | 004.442 AHO Data Structures and Algorithms |
Rosenblatt's perceptron --
Model building through regression --
The least-mean-square algorithm --
Multilayer perceptrons --
Kernel methods and radial-basis function networks --
Support vector machines --
Regularization theory --
Principal-components analysis --
Self-organizing maps --
Information-theoretic learning models -- Stochastic methods rooted in statistical mechanics --
Dynamic programming -- Neurodynamics --
Bayseian filtering for state estimation of dynamic systems --
Dynamically driven recurrent networks.
Using a wealth of case studies to illustrate the real-life, practical applications of neural networks, this state-of-the-art text exposes students to many facets of Neural Networks
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