| Titre : | NEURAL NET WORK |
| Auteurs : | Simon haykin, Auteur |
| Type de document : | texte imprimé |
| Editeur : | Prentice Hall PTR, 1999 |
| ISBN/ISSN/EAN : | 978-0-13-273350-2 |
| Format : | 842 P / 29 CM |
| Note générale : | NEURAL NET WORK OR ARTIFICIAL NEURAL NET WORK TO BE MORE PRECISE REPRESENT A TECHNOLOGY THAT IS ROOTED IN MANY DICIPLINES: NEUROSCIENCES MATHIMATICS STATISTICS PHYSICS COMPUTER SCIENCE AND ENGINEERING NEURAL NET WORK FIND APPLICATION IN SUCH DIVERSE FIELDS AS MODELING TIME SERIES ANALYSIS PATTERN RECOGNITION SIGNAL PROCESSING AND CONTROL BY VERTUE OF AN IMPORTANT PROPERTY THE ABILITY TO LEARN FROM INPUT DATA WITH OR WITHOUT A TEACHER |
| Langues: | Anglais |
| Résumé : |
1-INTRODUCTION 2-LEARNING PROCESSES 3-SINGLE LAYER PERCEPTRONS 4-MULILAYER PERCEPTRONS 5-RADIAL-BASIS FUNCTION NETWORKS 6-SUPPORT CECTOR MACHINES 7-COMMITTEE MACHINES 8-PRINCIPAL COMPONENTS ANALYSIS 9-SELF-ORGANIZING MAPS 10-INFORMATION-THEORITIC MODELS 11-STOCHASTIC MACHINES AND THEIR APPROXIMATES |
Exemplaires (1)
| Code-barres | Cote | Support | Localisation | Section | Disponibilité |
|---|---|---|---|---|---|
| TEC014796 | INF05209 | Livre | Fonds propre-bibliotheque centrale | Informatique | Libre accès Disponible |
