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

Harry Wechsler,Vladimir Cherkassky,Jerome H. Friedman

ISBN 10: 3540581995 / ISBN 13: 9783540581994

Editore: Springer, 1994

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• Titolo: From Statistics to Neural Networks: Theory and Pattern Recognition Applications (Nato ASI Subseries F:)
• Editore: Springer
• Data di pubblicazione: 1994
• Lingua: Inglese
• ISBN 10: 3540581995
• ISBN 13: 9783540581994
• Rilegatura: paperback
• Condizione: Very Good
• Tipo di libro: book

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Riassunto

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.