| Titre : | Neural networks for pattern recognition |
| Auteurs : | Christopher M. BISHOP |
| Type de document : | Texte imprimé |
| Editeur : | Oxford University Press, 2005 |
| Importance matérielle : | 482 p. |
| ISBN/ISSN/EAN : | 978-0-19-853864-6 |
| Langues: | Anglais |
| Index. décimale : | 221.20 |
| Mots-clés : |
[Classement] INFORMATIQUE [Aciège 2017] INTELLIGENCE ARTIFICIELLE [Aciège 2017] RESEAU INFORMATIQUE [Aciège 2017] RESEAU |
| Résumé : | In this texbook, the author provides the first comprehensive treatment of feedforward networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, he describes techniques for modelling probability density functions, and discusses the properties and relative merits of them ulti-layer perceptron and radial basis function network models. He also motivates the use of various forms of erro function, and reviews the principal algorithms for error function minimization. There is a detailed discussion of learning and generalization in neural networks, and the important topics of data processing, feature extraction, and prior knowledge are also covered. He concludes with an extensive treatment of Bayesian techniques and their applications to neural networks. |
Exemplaires (1)
| Code-barres | Cote | Support | Emplacement | Disponibilité |
|---|---|---|---|---|
| 029924 | 221.20 BIS | Livre | Salle de lecture | Disponible |
