On-Line Learning in Neural Networks

Table of Contents

Foreword C. Bishop
1. Introduction D. Saad
2. On-line learning and stochastic approximations Léon Bottou
3. Exact and perturbative solutions for the ensemble dynamics Todd Leen
4. A statistical study of on-line learning Noboru Murata
5. On-line learning in switching and drifting environments Klaus-Robert Mueller, Andreas Ziehe, Noboru Murata and Shun-ichi Amari
6. Parameter adaptation in stochastic optimization Luis B. Almeida, Thibault Langlois, José D. Amaral and Alexander Plakhov
7. Optimal on-line learning for multilayer neural networks David Saad and Magnus Rattray
8. Universal asymptotics in committee machines with tree architecture Mauro Copelli and Nestor Caticha
9. Incorporating curvature information in on-line learning Magnus Rattray and David Saad
10. Annealed on-line learning in multilayer networks Siegfried Bös and Shun-ichi Amari
11. On-line learning of prototypes and principal components Michael Biehl, Ansgar Freking, Matthias Hölzer, Georg Reents and Enno Schlösser
12. On-line learning with time-correlated patterns Tom Heskes and Wim Wiegerinck
13. On-line learning from finite training sets David Barber and Peter Sollich
14. Dynamics of supervised learning with restricted training sets Anthony C. C. Coolen and David Saad
15. On-line learning of a decision boundary with and without queries Yoshiyuki Kabashima and Shigeru Shinomoto
16. A Bayesian approach to on-line learning Manfred Opper
17. Optimal perception learning: an on-line Bayesian approach Sara A. Solla and Ole Winther.

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• Table of Contents (86 KB)
• Marketing Excerpt (705 KB)
• Front Matter (258 KB)
• Copyright Information Page (59 KB)