Synopsis of `Computational Learning Theory: An Introduction'

This book concentrates on the `probably approximately correct' (PAC) model of learning, and gradually develop the ideas of efficiency considerations. Applications of the theory to artificial neural networks are briefly considered. Exercises are included throughout. The book is quite self-contained, as the necessary background material from logic, probability and complexity theory is included. It should therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics.

For a more detailed account of the PAC model and its generalizations, with an emphasis on applications to artificial neural networks, see `Neural Network Learning: Theoretical Foundations'.

Some reviews:

"notable for its clean, readable, self-contained treatment of the foundations of PAC learnability theory" Computing Reviews.

"The book is well-written: every notion is precisely described and sufficiently illustrated by examples." Zentralblatt MATH.

"The book is very well written. ... The notions are illustrated with many examples and the proofs are presented in a very clear way" Journal of Logic and Computation.

"the choice of topics is good, covering most of the central issues" Mathematical Reviews.

Detailed Table of Contents