Computational Learning Theory

This an introduction to the theory of computational learning.

List of contents

1. Concepts, hypotheses, learning algorithms; 2. Boolean formulae and representations; 3. Probabilistic learning; 4. Consistent algorithms and learnability; 5. Efficient learning I; 6. Efficient learning II; 7. The VC dimension; 8. Learning and the VC dimension; 9. VC dimension and efficient learning; 10. Linear threshold networks.

Summary

This is a self contained volume in which the authors concentrate on the 'probably approximately correct model'. It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics.

Product details

• Authors: M. H. G. Anthony, N. Biggs, Norman L. Biggs
• Assisted by: C. J. van Rijsbergen (Editor), C. J. van Rijsbergen (Editor)
• Publisher: Cambridge University Press
• Languages: English
• Product format: Paperback / Softback
• Released: 10.01.2011
• EAN: 9780521599221
• ISBN: 978-0-521-59922-1
• No. of pages: 172
• Dimensions: 170 mm x 244 mm x 10 mm
• Weight: 309 g
• Series: Cambridge Tracts in Theoretica
• Subjects: Guides; Natural sciences, medicine, IT, technology > IT, data processing > IT