From Statistics to Neural Networks
Produktnummer:
185980c49811cf4c6d81662eaa7572bfdf
Themengebiete: | Classification Generalisierung Generalization Machine Learning Maschinelles Lernen Neural Networks Neuronale Netze Nichtparametrische Schätzung Nonparametric Estimation Pattern Recognition |
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Veröffentlichungsdatum: | 22.12.2011 |
EAN: | 9783642791215 |
Sprache: | Englisch |
Seitenzahl: | 394 |
Produktart: | Kartoniert / Broschiert |
Herausgeber: | Cherkassky, Vladimir Friedman, Jerome H. Wechsler, Harry |
Verlag: | Springer Berlin |
Untertitel: | Theory and Pattern Recognition Applications |
Produktinformationen "From Statistics to Neural Networks"
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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