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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'I recommend this book to readers with a theoretical, analytical, or mathematical interest in neural networks, especially online learning.' Computing Reviews