Neural networks and learning machines Simon Haykin

By:

Haykin, Simon

Material type: Text

TextPublication details: Noida Pearson 2016Edition: 3/eDescription: 909p. ill., 24cmISBN:

9789332570313

Subject(s):

DDC classification:

004.89 HAY

Contents:

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.

Summary: 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

List(s) this item appears in: New Arrivals December 2021

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Holdings
Item type	Current library	Collection	Call number	Status	Barcode
Books	IIITDM Kurnool General Stacks	Non-fiction	004.89 HAY (Browse shelf(Opens below))	Available	0004022
Reference	IIITDM Kurnool Reference	Reference	004.89 HAY (Browse shelf(Opens below))	Not For Loan	0004023
Books	IIITDM Kurnool General Stacks		004.89 HAY (Browse shelf(Opens below))	Available	0001289
Books	IIITDM Kurnool General Stacks		004.89 HAY (Browse shelf(Opens below))	Available	0001290
Books	IIITDM Kurnool General Stacks		004.89 HAY (Browse shelf(Opens below))	Available	0001291

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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