From Statistics to Neural Networks: Theory and Pattern Recognition Applications / Edition 1

From Statistics to Neural Networks: Theory and Pattern Recognition Applications / Edition 1

by Vladimir Cherkassky
     
 

ISBN-10: 3642791212

ISBN-13: 9783642791215

Pub. Date: 12/22/2011

Publisher: Springer Berlin Heidelberg

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…  See more details below

Overview

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.

Product Details

ISBN-13:
9783642791215
Publisher:
Springer Berlin Heidelberg
Publication date:
12/22/2011
Series:
Nato ASI Subseries F: (closed), #136
Edition description:
Softcover reprint of the original 1st ed. 1994
Pages:
394
Product dimensions:
6.10(w) x 9.25(h) x 0.03(d)

Table of 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.

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