Neural Networks

1. Front Matter

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2. When are k-nearest neighbor and back propagation accurate for feasible sized sets of examples?

   • Eric B. Baum

   Pages 1-25

3. Complexity theory of neural networks and classification problems

   • G. Cybenko

   Pages 26-44

4. Generalization performance of overtrained back-propagation networks

   • Yves Chauvin

   Pages 45-55

5. Stability of the random neural network model

   • Erol Gelenbe

   Pages 56-68

6. Temporal pattern recognition using EBPS

   • Marco Gori, Giovanni Soda

   Pages 69-80

7. Markovian spatial properties of a random field describing a stochastic neural network: Sequential or parallel implementation?

   • Thierry Herve, Olivier Francois, Jacques Demongeot

   Pages 81-89

8. Chaos in neural networks

   • Steve Renals

   Pages 90-99

9. The “moving targets” training algorithm

   • Richard Rohwer

   Pages 100-109

10. Acceleration techniques for the backpropagation algorithm

    • Fernando M. Silva, Luís B. Almeida

    Pages 110-119

11. Rule-injection hints as a means of improving network performance and learning time

    • S. C. Suddarth, Y. L. Kergosien

    Pages 120-129

12. Inversion in time

    • Sebastian Thrun, Alexander Linden

    Pages 130-140

13. Cellular neural networks: Dynamic properties and adaptive learning algorithm

    • Lieven Vandenberghe, Shaohua Tan, Joos Vandewalle

    Pages 141-150

14. Improved simulated annealing, Boltzmann machine, and attributed graph matching

    • Lei Xu, Erkki Oja

    Pages 151-160

15. Artificial dendritic learning

    • Tony Bell

    Pages 161-174

16. A neural net model of human short-term memory development

    • Gordon D. A. Brown

    Pages 175-185

17. Large vocabulary speech recognition using neural-fuzzy and concept networks

    • Nobuo Hataoka, Akio Amano, Toshiyuki Aritsuka, Akira Ichikawa

    Pages 186-196

18. Speech feature extraction using neural networks

    • Mahesan Niranjan, Frank Fallside

    Pages 197-204

19. Neural network based continuous speech recognition by combining self organizing feature maps and Hidden Markov Modeling

    • G. Rigoll

    Pages 205-214

20. Ultra-small implementation of a neural halftoning technique

    • T. Bernard, P. Garda, F. Devos, B. Zavidovique

    Pages 215-224

The EURASIP workshop contributions collected in this volume have an interdisciplinary character. The authors include psychologists, biologists, engineers and mathematicians as well as computer scientists. The volume starts with two invited papers, by George Cybenko and by Eric Baum, on the formal study of the capabilities of neural networks. The following papers are organized into parts dealing with theory and algorithms, speech processing, image processing, and implementation. The workshop was sponsored by the European Association for Signal Processing without restriction on the origin of participants.

• Backpropagation
• Bildverarbeitung
• Hidden Markov Model
• Neuronale Netze
• Siganlverarbeitung
• Simulation
• Sprachverarbeitung
• Verkettung
• cognition
• learning
• modeling
• neural network
• proving
• speech processing
• speech recognition

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