Learning Theory
An Approximation Theory Viewpoint
Series: Cambridge Monographs on Applied and Computational Mathematics; 24;
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Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
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Product details:
- Publisher Cambridge University Press
- Date of Publication 29 March 2007
- ISBN 9780521865593
- Binding Hardback
- No. of pages238 pages
- Size 231x160x17 mm
- Weight 360 g
- Language English
- Illustrations 20 b/w illus. 0
Categories
Short description:
A general overview of theoretical foundations; the first book to emphasize the approximation theory viewpoint.
MoreLong description:
The goal of learning theory is to approximate a function from sample values. To attain this goal learning theory draws on a variety of diverse subjects, specifically statistics, approximation theory, and algorithmics. Ideas from all these areas blended to form a subject whose many successful applications have triggered a rapid growth during the last two decades. This is the first book to give a general overview of the theoretical foundations of the subject emphasizing the approximation theory, while still giving a balanced overview. It is based on courses taught by the authors, and is reasonably self-contained so will appeal to a broad spectrum of researchers in learning theory and adjacent fields. It will also serve as an introduction for graduate students and others entering the field, who wish to see how the problems raised in learning theory relate to other disciplines.
'The book is well suited for its target audience, which includes researchers and graduate students. They will, no doubt, be reassured to find that each chapter closes with a collection of references and additional remarks, which place the preceding information in a wider context. ... Overall, this text is another excellent addition to the Applied and Computational Mathematics series published by Cambridge University Press. It complements other titles in the series without duplicating material and should be of value to anyone interested in learning theory or a neighbouring field.' Mathematics Today
Table of Contents:
Preface; Foreword; 1. The framework of learning; 2. Basic hypothesis spaces; 3. Estimating the sample error; 4. Polynomial decay approximation error; 5. Estimating covering numbers; 6. Logarithmic decay approximation error; 7. On the bias-variance problem; 8. Regularization; 9. Support vector machines for classification; 10. General regularized classifiers; Bibliography; Index.
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