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From Statistics to Neural Networks

Theory and Pattern Recognition Applications
BuchKartoniert, Paperback
394 Seiten
Englisch
Springererschienen am22.12.2011Softcover reprint of the original 1st ed. 1994
The ASI consisted of lectures overviewing major aspects of statistical and neural network learning, their links to biological learning and non-linear dynamics (chaos), and real-life examples of pattern recognition applications.mehr
Verfügbare Formate
BuchKartoniert, Paperback
EUR106,99
E-BookPDF1 - PDF WatermarkE-Book
EUR96,29

Produkt

KlappentextThe ASI consisted of lectures overviewing major aspects of statistical and neural network learning, their links to biological learning and non-linear dynamics (chaos), and real-life examples of pattern recognition applications.
Zusammenfassung
Details
ISBN/GTIN978-3-642-79121-5
ProduktartBuch
EinbandartKartoniert, Paperback
Verlag
Erscheinungsjahr2011
Erscheinungsdatum22.12.2011
AuflageSoftcover reprint of the original 1st ed. 1994
ReiheNATO ASI Subseries F:
Seiten394 Seiten
SpracheEnglisch
Gewicht628 g
IllustrationenXII, 394 p.
Artikel-Nr.18232947

Inhalt/Kritik

Inhaltsverzeichnis
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.mehr

Schlagworte