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Neural Networks for Pattern Recognition
by Chris Bishop and C. M. Bishop and Christopher M. Bishop


Overview - This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models.  Read more...

 
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More About Neural Networks for Pattern Recognition by Chris Bishop; C. M. Bishop; Christopher M. Bishop
 
 
 
Overview
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.


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Details
  • ISBN-13: 9780198538646
  • ISBN-10: 0198538642
  • Publisher: Clarendon Press
  • Publish Date: January 1996
  • Page Count: 504
  • Dimensions: 9.2 x 6.21 x 1.08 inches
  • Shipping Weight: 1.71 pounds

Series: Advanced Texts in Econometrics (Paperback)

Related Categories

Books > Computers > Optical Data Processing
Books > Computers > Neural Networks
Books > Computers > Human-Computer Interaction (HCI)

 
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