From Statistics to Neural Networks

1. Front Matter

   Pages I-XII

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2. An Overview of Predictive Learning and Function Approximation

   • Jerome H. Friedman

   Pages 1-61

3. Nonparametric Regression and Classification Part I—Nonparametric Regression

   • T. J. Hastie, R. J. Tibshirani

   Pages 62-69

4. Nonparametric Regression and Classification Part II—Nonparametric Classification

   • T. J. Hastie, R. J. Tibshirani

   Pages 70-82

5. Neural Networks, Bayesian a posteriori Probabilities, and Pattern Classification

   • Richard P. Lippmann

   Pages 83-104

6. Flexible Non-linear Approaches to Classification

   • B. D. Ripley

   Pages 105-126

7. Parametric Statistical Estimation with Artificial Neural Networks: A Condensed Discussion

   • Halbert White

   Pages 127-146

8. Prediction Risk and Architecture Selection for Neural Networks

   • John Moody

   Pages 147-165

9. Regularization Theory, Radial Basis Functions and Networks

   • Federico Girosi

   Pages 166-187

10. Self-Organizing Networks for Nonparametric Regression

    • Vladimir Cherkassky, Filip Mulier

    Pages 188-212

11. Neural Preprocessing Methods

    • Luís B. Almeida

    Pages 213-225

12. Improved Hidden Markov Models for Speech Recognition Through Neural Network Learning

    • Chris J. Wellekens

    Pages 226-242

13. Neural Network Architectures for Pattern Recognition

    • Françoise Fogelman Soulié

    Pages 243-262

14. Cooperative Decision Making Processes and Their Neural Net Implementation

    • J. Kittler

    Pages 263-281

15. Associative Memory Networks and Sparse Similarity Preserving Codes

    • Günther Palm, Friedhelm Schwenker, Friedrich T. Sommer

    Pages 282-302

16. Multistrategy Learning and Optimal Mappings

    • H. Wechsler

    Pages 303-318

17. Self-Organizing Neural Networks for Supervised and Unsupervised Learning and Prediction

    • Gail A. Carpenter, Stephen Grossberg

    Pages 319-348

18. Recognition of 3-D Objects from Multiple 2-D Views by a Self-Organizing Neural Architecture

    • Gary Bradski, Stephen Grossberg

    Pages 349-375

19. Chaotic Dynamics in Neural Pattern Recognition

    • Walter J. Freeman

    Pages 376-394

20. Back Matter

    Pages 395-401

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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.

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

• Department of Electrical Engineering, University of Minnesota, Minneapolis, USA

  Vladimir Cherkassky

• Department of Statistics, Stanford University, Stanford, USA

  Jerome H. Friedman

• Computer Science Department, George Mason University, Fairfax, USA

  Harry Wechsler

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