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Learning and Generalisation : With Applications to Neural Networks /

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Learning and Generalization provides a formal mathematical theory for addressing intuitive questions such as: How does a machine learn a new concept on the basis of examples? How can a neural network, after sufficient training, correctly predict the outcome of a previously unseen input? How much tra...

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Bibliographic Details
Main Author: Vidyasagar, M.
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: London : Springer London : Imprint : Springer, 2003.
Series:Communications and control engineering.
Subjects:
Engineering.
Computer Communication Networks.
Group theory.
System theory.
Distribution (Probability theory)
Engineering mathematics.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Introduction
  • Preliminaries
  • Problem Formulations
  • Vapnik-Chervonenkis, Pseudo- and Fat-Shattering Dimensions
  • Uniform Convergence of Empirical Means
  • Learning Under a Fixed Probability Measure
  • Distribution-Free Learning
  • Learning Under an Intermediate Family of Probabilities
  • Alternate Models of Learning
  • Applications to Neural Networks
  • Applications to Control Systems
  • Some Open Problems.