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From Statistics to Neural Networks

Theory and Pattern Recognition Applications

  • Conference proceedings
  • © 1994

Overview

Editors:
  1. Vladimir Cherkassky

    1. Department of Electrical Engineering, University of Minnesota, Minneapolis, USA

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  2. Jerome H. Friedman

    1. Department of Statistics, Stanford University, Stanford, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Harry Wechsler

    1. Computer Science Department, George Mason University, Fairfax, USA

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Part of the book series: NATO ASI Subseries F: (NATO ASI F, volume 136)

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Table of contents (18 papers)

  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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Keywords

About this book

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.

Editors and Affiliations

  • 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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