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Artificial Neural Networks - ICANN 96

6th International Conference, Bochum, Germany, July 16 - 19, 1996. Proceedings

  • Conference proceedings
  • © 1996

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Editors:
  1. Christoph Malsburg
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  2. Werner Seelen
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  3. Jan C. Vorbrüggen
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  4. Bernhard Sendhoff
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 1112)

Included in the following conference series:

Conference proceedings info: ICANN 1996.

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About this book

This book constitutes the refereed proceedings of the sixth International Conference on Artificial Neural Networks - ICANN 96, held in Bochum, Germany in July 1996.
The 145 papers included were carefully selected from numerous submissions on the basis of at least three reviews; also included are abstracts of the six invited plenary talks. All in all, the set of papers presented reflects the state of the art in the field of ANNs. Among the topics and areas covered are a broad spectrum of theoretical aspects, applications in various fields, sensory processing, cognitive science and AI, implementations, and neurobiology.

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Table of contents (152 papers)

  1. Front Matter

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  2. Application of Artificial Neural Networks in Particle Physics

    • Hermann Kolanoski
    Pages 1-14

  3. Evolutionary computation — History, status, and perspectives

    • H. P. Schwefel
    Pages 15-15

  4. Temporal structure of cortical activity

    • M. Abeles, Y. Prut
    Pages 16-16

  5. SEE-1 — A vision system for use in real world environments

    • U. Ramacher
    Pages 17-17

  6. Towards integration of nerve cells and silicon devices

    • P. Fromherz
    Pages 18-18

  7. Unifying perspectives on neuronal codes and processing

    • C. H. Anderson
    Pages 19-19

  8. Visual recognition based on coding in temporal cortex: Analysis of pattern configuration and generalisation across viewing conditions without mental rotation

    • D. I. Perrett
    Pages 20-20

  9. A novel encoding strategy for associative memory

    • Han-Bing Ji, Kwong-Sak Leung, Yee Leung
    Pages 21-27

  10. Autoassociative memory with high storage capacity

    • Paulo J. L. Adeodato, John G. Taylor
    Pages 29-34

  11. Information efficiency of the associative net at arbitrary coding rates

    • Bruce Graham, David Willshaw
    Pages 35-40

  12. Efficient learning in sparsely connected Boltzmann machines

    • Marcel J. Nijman, Hilbert J. Kappen
    Pages 41-46

  13. Incorporating invariances in support vector learning machines

    • Bernhard Schölkopf, Chris Burges, Vladimir Vapnik
    Pages 47-52

  14. Estimating the reliability of neural network classifications

    • Aarnoud Hoekstra, Servaes A. Tholen, Robert P. W. Duin
    Pages 53-58

  15. Bayesian inference of noise levels in regression

    • Christopher M. Bishop, Cazhaow S. Qazaz
    Pages 59-64

  16. Complexity reduction in probabilistic neural networks

    • Ari Hämäläinen, Lasse Holmström
    Pages 65-70

  17. Asymptotic complexity of an RBF NN for correlated data representation

    • Lucia Sardo, Josef Kittler
    Pages 71-76

  18. Regularization by early stopping in single layer perceptron training

    • Sarunas Raudys, Tautvydas Cibas
    Pages 77-82

  19. Clustering in weight space of feedforward nets

    • Stefan M. Rüger, Arnfried Ossen
    Pages 83-88

  20. Learning curves of on-line and off-line training

    • Siegfried Bös
    Pages 89-94
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