Neural Networks for Pattern Recognition
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Description: 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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All the information you need in one place! Each Study Brief is a summary of one specific subject; facts, figures, and explanations to help you learn faster.
List price: $105.00
Copyright year: 1995
Publisher: Oxford University Press, Incorporated
Publication date: 1/18/1996
Size: 6.25" wide x 9.25" long x 1.25" tall
Geoffrey Hinton is Professor of Computer Science at the University of Toronto.
|Statistical pattern recognition|
|Probability density estimation|
|The multi-layer perceptron|
|Radial basis functions|
|Parameter optimization algorithms|
|Pre-processing and feature extraction|
|Learning and generalization|