© 2003

Learning and Generalisation

With Applications to Neural Networks

  • Comprehensive; this book covers all aspects of learning theory and its applications. Other books have a narrower focus

  • It contains applications not only to neural networks but also to control systems

  • The author has recently been selected to receive the Hendrik W. Bode Lecture Prize awarded by the IEEE Control Systems Society


Part of the Communications and Control Engineering book series (CCE)

Table of contents

  1. Front Matter
    Pages i-xxi
  2. M. Vidyasagar
    Pages 1-11
  3. M. Vidyasagar
    Pages 13-41
  4. M. Vidyasagar
    Pages 43-113
  5. M. Vidyasagar
    Pages 149-205
  6. M. Vidyasagar
    Pages 207-253
  7. M. Vidyasagar
    Pages 255-283
  8. M. Vidyasagar
    Pages 311-363
  9. M. Vidyasagar
    Pages 365-420
  10. M. Vidyasagar
    Pages 421-463
  11. M. Vidyasagar
    Pages 465-474
  12. Back Matter
    Pages 475-488

About this book


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 training is required to achieve a specified level of accuracy in the prediction?

• How can one identify the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite interval of time?

In its successful first edition, A Theory of Learning and Generalization was the first book to treat the problem of machine learning in conjunction with the theory of empirical processes, the latter being a well-established branch of probability theory. The treatment of both topics side-by-side leads to new insights, as well as to new results in both topics.

This second edition extends and improves upon this material, covering new areas including:

• Support vector machines.

• Fat-shattering dimensions and applications to neural network learning.

• Learning with dependent samples generated by a beta-mixing process.

• Connections between system identification and learning theory.

• Probabilistic solution of 'intractable problems' in robust control and matrix theory using randomized algorithm.

Reflecting advancements in the field, solutions to some of the open problems posed in the first edition are presented, while new open problems have been added.

Learning and Generalization (second edition) is essential reading for control and system theorists, neural network researchers, theoretical computer scientists and probabilist.


Computer Control Theory Robust Control Stochastic Processes Support Vector Machine Support Vector Machines System Identif UCEM algorithm algorithms artificial intelligence learning machine learning matrix theory reading

Authors and affiliations

  1. 1.Tata Consultancy ServicesSecunderabadIndia

Bibliographic information

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