Computational Learning Theory

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Paperback | 28 April 1997

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172 Pages

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24.41 x 16.99 x 0.94

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Computational learning theory is one of the first attempts to construct a mathematical theory of a cognitive process. It has been a field of much interest and rapid growth in recent years. This text provides a framework for studying a variety of algorithmic processes, such as those currently in use for training artificial neural networks. The authors concentrate on an approximate model for learning and gradually develop the ideas of efficiency considerations. Finally, they consider applications of the theory to artificial neural networks. An abundance of exercises and an extensive list of references round out the text. This volume provides a comprehensive review of the topic, including information drawn from logic, probability, and complexity theory. It forms a solid introduction to the theory of comptutational learning suitable for a broad spectrum of graduate students from theoretical computer science to mathematics.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Machine Learning Computer Programming & Software Development Computer Vision

Concepts, hypotheses, learning algorithms
Boolean formulae and representations
Probabilistic learning
Consistent algorithms and learnability
Efficient learning I
Efficient learning II
The VC dimension
Learning and the VC dimension
VC dimension and efficient learning
Linear threshold networks
Table of Contents provided by Publisher. All Rights Reserved.

Computational Learning Theory

At a Glance

Paperback

Shipping

How to return your order

Defective items

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