Résumé
Learning is one of the things that human s do naturally,
and it has always been a challenge for us to understand the
process. Nowadays this challenge has another dimension as
we try to build machines that are able to learn and to
undertake tasks such as datamining, image processing and
pattern recognition. We can formulate a simple framework,
artificial neural networks, in which learning from examples
may be described and understood. The contribution to this
subject made over the last decade by researchers applying
the techniques of statistical mechanics is the subject of
this book. The authors provide a coherent account of
various important concepts and techniques that are
currently only found scattered in papers, supplement this
with background material in mathematics and physics, and
include many examples and exercises to make a book that can
be used with courses, or for self-teaching, or as a handy
reference.
Contents
- Getting started
- Perceptron learning - Basics
- A choice of learning rules
- Augmented statistical mechanics formulation
- Noisy teachers
- The storage problem
- Discontinuous learning
- Unsupervised leraning
- On-line learning
- Making contact with statistic
- A bird's eye view : multifractals
- Multilayer networks
- On-line learning in multilayer networks
- What else ?
- Appendices
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Cambridge University Press |
| Auteur(s) | A. Engel, C. Van den Broeck |
| Parution | 01/04/2001 |
| Nb. de pages | 329 |
| Format | 17,3 x 24,6 |
| Couverture | Broché |
| Poids | 694g |
| Intérieur | Noir et Blanc |
| EAN13 | 9780521774796 |
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