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Neural Networks and Learning Machines

Neural Networks and Learning Machines

Author(s):
  • Simon Haykin
    • Author: Simon Haykin
    • ISBN:9789332570313
    • 10 Digit ISBN:9332570310
    • Price:Rs. 960.00
    • Pages:944
    • Imprint:Pearson Education
    • Binding:Paperback
    • Status:Available


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    Refocused, 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.

     

    Table of Content

    Chapter 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
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    Salient Features

    • Based on the latest version of MATLAB®
    • More than 30 graphs in color in the chapter ""MATLAB® Graphics""
    • List of commands at the end of the chapter for quick recapitulation
    • Appendices on graphic user interface and control system analysis using the LTI viewer
    • Approximately 250 figures and screenshots
    • Programming tips to highlight good programming practices
    • More than 250 solved examples and approximately 200 end-of-chapter exercises."