Learning and Generalisation
With Applications to Neural Networks
2nd Edition
Published on 14. March 2013
XXI, 488 pages
978-1-4471-3748-1 (ISBN)
Description
The author is extremely well known and respected in this field and he provides a very comprehensive text with a broad focus covering all aspects of learning theory and it's applications.
More details
Series
Edition
Second Edition 2003
Language
English
Place of publication
London
United Kingdom
Publishing group
Springer London
Target group
Professional and scholarly
Illustrations
XXI, 488 p.
File size
44,06 MB
ISBN-13
978-1-4471-3748-1 (9781447137481)
DOI
10.1007/978-1-4471-3748-1
Schweitzer Classification
Other editions
Additional editions
Book
10/2010
2nd Edition
Springer
€192.59
Shipment within 15-20 days
Book
09/2002
2nd Edition
Springer
€192.59
Shipment within 15-20 days
Content
1. Introduction.- 2. Preliminaries.- 3. Problem Formulations.- 4. Vapnik-Chervonenkis, Pseudo- and Fat-Shattering Dimensions.- 5. Uniform Convergence of Empirical Means.- 6. Learning Under a Fixed Probability Measure.- 7. Distribution-Free Learning.- 8. Learning Under an Intermediate Family of Probabilities.- 9. Alternate Models of Learning.- 10. Applications to Neural Networks..- 11. Applications to Control Systems.- 12. Some Open Problems.
