MIT Press (1996) ORDERING INFO:
ISBN 0-262-07174-6 http://www.amazon.com
Cloth ($65.00), 432 pages. http://www-mitpress.mit.edu/order-info.html
1-800-356-0343 (MIT BOOK ORDER Department)
***FOR MORE INFO: http://www.utdallas.edu/~golden/book_abs.html
This textbook teaches students how to carefully use a powerful set of
mathematical tools for analyzing and designing a wide variety of
NONLINEAR HIGH-DIMENSIONAL Artificial Neural Network (ANN) systems.
Chapter 1: ANN systems with Neuroscience, Psychology, Engineering Applications
Chapter 2: Specific ANN system architectures; classification/learning paradigms
Chapter 3: LaSalle's Invariant Set Theorem for behavioral analysis of ANNs
Chapter 4: Stochastic Approximation Theorem for behavioral analysis of ANNs
Chapter 5: Nonlinear Optimization Theory for ANN system design
Chapters 6,7: Bayesian Decision Theory and Markov Random Fields for the
design of "rational" ANN classification/learning objective functions.
Chapter 8: Confidence intervals for an ANN system's predictions. Statistical
tests for: (i) pruning/adding units, and (ii) model selection.
Solutions: Solutions to over 100 ANN system analysis and design problems
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