From Statistics to Neural Networks
Subtitle: Theory and Pattern Recognition Applications
By: edited by Vladimir Cherkassky, Jerome H. Friedman, Harry Wechsler
Contributors: Friedman, J. H. (Jerome H.); Wechsler, Harry
Series: NATO ASI Series, Series F: Computer and Systems Sciences -- 136; NATO ASI series -- 136.
Publisher: Springer Berlin Heidelberg
Published: 1994
Place of publication: Berlin, Heidelberg
Language: eng
Format: [electronic resource] :
Number of pages: 394
ISBN-13: 9783642791192
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
Description: 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.