Neural networks and learning machines
Haykin, Simon
Neural networks and learning machines Simon Haykin - 3/e - Noida Pearson 2016 - 909p. ill., 24cm
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
9789332570313
Neural networks (Computer science)
Neural networks (Computer science) -- Problems, exercises, etc.
Lernendes System
004.89 / HAY
Neural networks and learning machines Simon Haykin - 3/e - Noida Pearson 2016 - 909p. ill., 24cm
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
9789332570313
Neural networks (Computer science)
Neural networks (Computer science) -- Problems, exercises, etc.
Lernendes System
004.89 / HAY