For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science.
Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised.
Auflage
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Maße
Höhe: 244 mm
Breite: 181 mm
Dicke: 35 mm
Gewicht
ISBN-13
978-0-13-273350-2 (9780132733502)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
1. Introduction.
2. Learning Processes.
3. Single-Layer Perceptrons.
4. Multilayer Perceptrons.
5. Radial-Basis Function Networks.
6. Support Vector Machines.
7. Committee Machines.
8. Principal Components Analysis.
9. Self-Organizing Maps.
10. Information-Theoretic Models.
11. Stochastic Machines & Their Approximates Rooted in Statistical Mechanics.
12. Neurodynamic Programming.
13. Temporal Processing Using Feedforward Networks.
14. Neurodynamics.
15. Dynamically Driven Recurrent Networks.
Epilogue.
Bibliography.
Index.
