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From Statistics to Neural Networks : Theory and Pattern Recognition Applications

Author: Vladimir Cherkassky; J H Friedman; Harry Wechsler
Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 1994.
Series: NATO ASI series., Series F,, Computer and systems sciences ;, 136.
Edition/Format:   eBook : Bibliographic data : EnglishView all editions and formats
Summary:
This volume provides a unified approach to the study of predictive learning, i.e., generalization from examples. It contains an up-to-date review and in-depth treatment of major issues and methods related to predictive learning in statistics, Artificial Neural Networks (ANN), and pattern recognition. Topics range from theoretical modeling and adaptive computational methods to empirical comparisons between  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Printed edition:
Material Type: Bibliographic data, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Vladimir Cherkassky; J H Friedman; Harry Wechsler
ISBN: 9783642791192 3642791190 9783642791215 3642791212
OCLC Number: 840299996
Description: 1 online resource (XII, 394 pages).
Contents: An Overview of Predictive Learning and Function Approximation --
Nonparametric Regression and Classification Part I Nonparametric Regression --
Nonparametric Regression and Classification Part II Nonparametric Classification --
Neural Networks, Bayesian a posteriori Probabilities, and Pattern Classification --
Flexible Non-linear Approaches to Classification --
Parametric Statistical Estimation with Artificial Neural Networks: A Condensed Discussion --
Prediction Risk and Architecture Selection for Neural Networks --
Regularisation Theory, Radial Basis Functions and Networks --
Self-Organizing Networks for Nonparametric Regression --
Neural Preprocessing Methods --
Improved Hidden Markov Models for Speech Recognition Through Neural Network Learning --
Neural Network Architectures for Pattern Recognition --
Cooperative Decision Making Processes and Their Neural Net Implementation --
Associative Memory Networks and Sparse Similarity Preserving Codes --
Multistrategy Learning and Optimal Mappings --
Self-Organizing Neural Networks for Supervised and Unsupervised Learning and Prediction --
Recognition of 3-D Objects from Multiple 2-D Views by a Self-Organizing Neural Architecture --
Chaotic Dynamics in Neural Pattern Recognition.
Series Title: NATO ASI series., Series F,, Computer and systems sciences ;, 136.
Other Titles: Proceedings of the NATO Advances Study Institute From Statistics to Neural Networks, Theory and Pattern Recognition Applications, held in Les Arcs, Bourg Saint Maurice, France, June 21 - July 2, 1993
Responsibility: edited by Vladimir Cherkassky, Jerome H. Friedman, Harry Wechsler.

Abstract:

Proceedings of the NATO Advances Study Institute From Statistics to Neural Networks, Theory and Pattern Recognition Applications, held in Les Arcs, Bourg Saint Maurice, France, June 21 - July 2, 1993  Read more...

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