Dekorationsartikel gehören nicht zum Leistungsumfang.
Neural Network Learning
Theoretical Foundations
Taschenbuch von Martin Anthony (u. a.)
Sprache: Englisch

64,50 €*

inkl. MwSt.

Versandkostenfrei per Post / DHL

Aktuell nicht verfügbar

Kategorien:
Beschreibung
This book describes theoretical advances in the study of artificial neural networks.
This book describes theoretical advances in the study of artificial neural networks.
Inhaltsverzeichnis
1. Introduction; Part I. Pattern Recognition with Binary-output Neural Networks: 2. The pattern recognition problem; 3. The growth function and VC-dimension; 4. General upper bounds on sample complexity; 5. General lower bounds; 6. The VC-dimension of linear threshold networks; 7. Bounding the VC-dimension using geometric techniques; 8. VC-dimension bounds for neural networks; Part II. Pattern Recognition with Real-output Neural Networks: 9. Classification with real values; 10. Covering numbers and uniform convergence; 11. The pseudo-dimension and fat-shattering dimension; 12. Bounding covering numbers with dimensions; 13. The sample complexity of classification learning; 14. The dimensions of neural networks; 15. Model selection; Part III. Learning Real-Valued Functions: 16. Learning classes of real functions; 17. Uniform convergence results for real function classes; 18. Bounding covering numbers; 19. The sample complexity of learning function classes; 20. Convex classes; 21. Other learning problems; Part IV. Algorithmics: 22. Efficient learning; 23. Learning as optimisation; 24. The Boolean perceptron; 25. Hardness results for feed-forward networks; 26. Constructive learning algorithms for two-layered networks.
Details
Erscheinungsjahr: 2009
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 404
ISBN-13: 9780521118620
ISBN-10: 052111862X
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Anthony, Martin
Bartlett, Peter L
Hersteller: European Community
Maße: 229 x 152 x 24 mm
Von/Mit: Martin Anthony (u. a.)
Erscheinungsdatum: 20.08.2009
Gewicht: 0,653 kg
preigu-id: 101498801
Inhaltsverzeichnis
1. Introduction; Part I. Pattern Recognition with Binary-output Neural Networks: 2. The pattern recognition problem; 3. The growth function and VC-dimension; 4. General upper bounds on sample complexity; 5. General lower bounds; 6. The VC-dimension of linear threshold networks; 7. Bounding the VC-dimension using geometric techniques; 8. VC-dimension bounds for neural networks; Part II. Pattern Recognition with Real-output Neural Networks: 9. Classification with real values; 10. Covering numbers and uniform convergence; 11. The pseudo-dimension and fat-shattering dimension; 12. Bounding covering numbers with dimensions; 13. The sample complexity of classification learning; 14. The dimensions of neural networks; 15. Model selection; Part III. Learning Real-Valued Functions: 16. Learning classes of real functions; 17. Uniform convergence results for real function classes; 18. Bounding covering numbers; 19. The sample complexity of learning function classes; 20. Convex classes; 21. Other learning problems; Part IV. Algorithmics: 22. Efficient learning; 23. Learning as optimisation; 24. The Boolean perceptron; 25. Hardness results for feed-forward networks; 26. Constructive learning algorithms for two-layered networks.
Details
Erscheinungsjahr: 2009
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 404
ISBN-13: 9780521118620
ISBN-10: 052111862X
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Anthony, Martin
Bartlett, Peter L
Hersteller: European Community
Maße: 229 x 152 x 24 mm
Von/Mit: Martin Anthony (u. a.)
Erscheinungsdatum: 20.08.2009
Gewicht: 0,653 kg
preigu-id: 101498801
Warnhinweis

Ähnliche Produkte

Ähnliche Produkte