From Statistics to Neural Networks

Theory and Pattern Recognition Applications

  • Vladimir Cherkassky
  • Jerome H. Friedman
  • Harry Wechsler
Conference proceedings

Part of the NATO ASI Series book series (volume 136)

Table of contents

  1. Front Matter
    Pages I-XII
  2. Vladimir Cherkassky, Filip Mulier
    Pages 188-212
  3. Luís B. Almeida
    Pages 213-225
  4. Françoise Fogelman Soulié
    Pages 243-262
  5. Günther Palm, Friedhelm Schwenker, Friedrich T. Sommer
    Pages 282-302
  6. Walter J. Freeman
    Pages 376-394
  7. Back Matter
    Pages 395-401

About these proceedings


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.


Classification Generalisierung Generalization Machine Learning Maschinelles Lernen Neural Networks Neuronale Netze Nichtparametrische Schätzung Nonparametric Estimation Pattern Recognition Regression Statistics classifikation

Editors and affiliations

  • Vladimir Cherkassky
    • 1
  • Jerome H. Friedman
    • 2
  • Harry Wechsler
    • 3
  1. 1.Department of Electrical EngineeringUniversity of MinnesotaMinneapolisUSA
  2. 2.Department of StatisticsStanford UniversityStanfordUSA
  3. 3.Computer Science DepartmentGeorge Mason UniversityFairfaxUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 1994
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-642-79121-5
  • Online ISBN 978-3-642-79119-2
  • Buy this book on publisher's site