• Sign In / Register
  • Help
  • Basket0
The books you love, the emails you want
Time is running out, opt in before 25 May or you'll stop hearing from us
Yes Please
Neural Network Learning: Theoretical Foundations (Hardback)
  • Neural Network Learning: Theoretical Foundations (Hardback)

Neural Network Learning: Theoretical Foundations (Hardback)

(author), (author)
Hardback 404 Pages / Published: 04/11/1999
  • We can order this

Usually despatched within 3 weeks

  • This item has been added to your basket
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.

Publisher: Cambridge University Press
ISBN: 9780521573535
Number of pages: 404
Weight: 760 g
Dimensions: 228 x 152 x 27 mm

You may also be interested in...

Not Exactly
Added to basket
Intelligence Emerging
Added to basket
Reinforcement Learning
Added to basket
Neuronal Dynamics
Added to basket


Please sign in to write a review

Your review has been submitted successfully.