From Statistics to Neural Networks : Theory and Pattern Recognition Applications

Edited by Cherkassky, Vladimir; Edited by Friedman, Jerome H.; Edited by Wechsler, Harry

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 statistical and ANN methods, and applications. Most contributions fall into one of the three themes: unified framework for the study of predictive learning in statistics and ANNs; similarities and differences between statistical and ANN methods for nonparametric estimation (learning); and fundamental connections between artificial and biological learning systems.

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[目次]

  • 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.

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この本の情報

書名 From Statistics to Neural Networks : Theory and Pattern Recognition Applications
著作者等 Friedman, Jerome H.
Wechsler, Harry
Cherkassky, Vladimir
書名別名 Theory and Pattern Recognition Applications
シリーズ名 NATO ASI Subseries F: 136
出版元 Springer-Verlag Berlin and Heidelberg GmbH & Co. K
刊行年月 2011.12.22
版表示 Softcover reprint of the original 1st ed. 1994
ページ数 416p
大きさ H234 x W156
ISBN 9783642791215
言語 英語
出版国 ドイツ
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