Meer over de auteurs

Martin Anthony, Martin (London School of Economics and Political Science) Anthony, Peter L. Bartlett, Peter L. (Australian National University, Canberra) Bartlett e.a.

Neural Network Learning

Name: Neural Network Learning
Author: Martin Anthony

Theoretical Foundations

Paperback Engels 2009 9780521118620

66,96

In winkelwagen

Verwachte levertijd ongeveer 9 werkdagen

Samenvatting

This book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Research on pattern classification with binary-output networks is surveyed, including a discussion of the relevance of the Vapnik–Chervonenkis dimension, and calculating estimates of the dimension for several neural network models. A model of classification by real-output networks is developed, and the usefulness of classification with a 'large margin' is demonstrated. The authors explain the role of scale-sensitive versions of the Vapnik–Chervonenkis dimension in large margin classification, and in real prediction. They also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient constructive learning algorithms. The book is self-contained and is intended to be accessible to researchers and graduate students in computer science, engineering, and mathematics.

Specificaties

ISBN13:9780521118620

Taal:Engels

Bindwijze:Paperback

Aantal pagina's:404

Uitgever:Cambridge University Press

Lezersrecensies

Wees de eerste die een lezersrecensie schrijft!

Schrijf een recensie

Uw cijfer

?

Log in om te stemmen

Inhoudsopgave

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.

Managementboek Top 100

Bekijk de volledige Managementboek Top 100

Uw winkelwagen