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Neural Networks and Learning Machines, 3/e


Neural Networks and Learning Machines, 3/e
Author(s)  Simon Haykin
ISBN  9789332570313
Imprint  Pearson Education
Copyright  2016
Pages  944
Binding  Paperback
List Price  Rs. 960.00
  
 
 

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.
 

  • About the Author
  • Contents
  • Features
  • Downloadable Resources

Simon O. Haykin, McMaster University, Ontario Canada


 

 

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


"


 

 

• 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."


 

 
Instructor Resources
 


 
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