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

Cherkassky, Vladimir

ISBN 10: 3540581995 / ISBN 13: 9783540581994

Editore: Springer, 1994

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Zustand: Sehr gut - Gepflegter, sauberer Zustand.1994. Aus der Auflösung einer renommierten Bibliothek. Kann Stempel beinhalten. | Seiten: 406 | Sprache: Englisch | Produktart: Bücher. Codice articolo 41478391/202

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Titolo: From Statistics to Neural Networks: Theory ...

Casa editrice: Springer

Data di pubblicazione: 1994

Legatura: Brossura

Condizione: Sehr gut

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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.