9783642791215-3642791212-From Statistics to Neural Networks: Theory and Pattern Recognition Applications (NATO ASI Subseries F:, 136)

From Statistics to Neural Networks: Theory and Pattern Recognition Applications (NATO ASI Subseries F:, 136)

ISBN-13: 9783642791215
ISBN-10: 3642791212
Edition: Softcover reprint of the original 1st ed. 1994
Author: Harry Wechsler, Jerome H. Friedman, Vladimir Cherkassky
Publication date: 2011
Publisher: Springer
Format: Paperback 406 pages
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Book details

ISBN-13: 9783642791215
ISBN-10: 3642791212
Edition: Softcover reprint of the original 1st ed. 1994
Author: Harry Wechsler, Jerome H. Friedman, Vladimir Cherkassky
Publication date: 2011
Publisher: Springer
Format: Paperback 406 pages

Summary

From Statistics to Neural Networks: Theory and Pattern Recognition Applications (NATO ASI Subseries F:, 136) (ISBN-13: 9783642791215 and ISBN-10: 3642791212), written by authors Harry Wechsler, Jerome H. Friedman, Vladimir Cherkassky, was published by Springer in 2011. With an overall rating of 4.2 stars, it's a notable title among other AI & Machine Learning (Graphics & Design, Graphics & Multimedia, Programming, Software Design, Testing & Engineering, Bioinformatics, Biological Sciences, Computer Science) books. You can easily purchase or rent From Statistics to Neural Networks: Theory and Pattern Recognition Applications (NATO ASI Subseries F:, 136) (Paperback) from BooksRun, along with many other new and used AI & Machine Learning books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.3.

Description

The NATO Advanced Study Institute From Statistics to Neural Networks, Theory and Pattern Recognition Applications took place in Les Arcs, Bourg Saint Maurice, France, from June 21 through July 2, 1993. The meeting brought to gether over 100 participants (including 19 invited lecturers) from 20 countries. The invited lecturers whose contributions appear in this volume are: L. Almeida (INESC, Portugal), G. Carpenter (Boston, USA), V. Cherkassky (Minnesota, USA), F. Fogelman Soulie (LRI, France), W. Freeman (Berkeley, USA), J. Friedman (Stanford, USA), F. Girosi (MIT, USA and IRST, Italy), S. Grossberg (Boston, USA), T. Hastie (AT&T, USA), J. Kittler (Surrey, UK), R. Lippmann (MIT Lincoln Lab, USA), J. Moody (OGI, USA), G. Palm (U1m, Germany), B. Ripley (Oxford, UK), R. Tibshirani (Toronto, Canada), H. Wechsler (GMU, USA), C. Wellekens (Eurecom, France) and H. White (San Diego, USA). 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. As a result of lively interactions between the participants, the following topics emerged as major themes of the meeting: (1) Unified framework for the study of Predictive Learning in Statistics and Artificial Neural Networks (ANNs); (2) Differences and similarities between statistical and ANN methods for non parametric estimation from examples (learning); (3) Fundamental connections between artificial learning systems and biological learning systems.
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