From Statistics to Neural Networks | Theory and Pattern Recognition Applications | ISBN 9783642791215

From Statistics to Neural Networks

Theory and Pattern Recognition Applications

herausgegeben von Vladimir Cherkassky, Jerome H. Friedman und Harry Wechsler
Mitwirkende
Herausgegeben vonVladimir Cherkassky
Herausgegeben vonJerome H. Friedman
Herausgegeben vonHarry Wechsler
Buchcover From Statistics to Neural Networks  | EAN 9783642791215 | ISBN 3-642-79121-2 | ISBN 978-3-642-79121-5

From Statistics to Neural Networks

Theory and Pattern Recognition Applications

herausgegeben von Vladimir Cherkassky, Jerome H. Friedman und Harry Wechsler
Mitwirkende
Herausgegeben vonVladimir Cherkassky
Herausgegeben vonJerome H. Friedman
Herausgegeben vonHarry Wechsler
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.