Neural Network Learning

Theoretical Foundations

By: Martin Anthony, Peter L. Bartlett

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Paperback | 20 August 2009

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404 Pages

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23.0 x 15.3 x 2.4

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This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, Anthony and Bartlett develop a model of classification by real-output networks, and demonstrate the usefulness of classification with a "large margin." The authors explain the role of scale-sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction. Key chapters also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient, constructive learning algorithms. The book is self-contained and accessible to researchers and graduate students in computer science, engineering, and mathematics.

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'The book is a useful and readable mongraph. For beginners it is a nice introduction to the subject, for experts a valuable reference.' Zentralblatt MATH

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You Can Find This Book In

Non-Fiction Psychology Physiological & Neuro-Psychology, Biopsychology Science Biology, Life Sciences Life Sciences in General Neurosciences Computing & I.T.Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Machine Learning

Computer Vision Computer Networking & Communications

Other Editions and Formats

Hardcover

Published: 3rd January 2000

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Introduction
Pattern Recognition with Binary-output Neural Networks:
The pattern recognition problem
The growth function and VC-dimension
General upper bounds on sample complexity
General lower bounds
The VC-dimension of linear threshold networks
Bounding the VC-dimension using geometric techniques
VC-dimension bounds for neural networks
Pattern Recognition with Real-output Neural Networks:
Classification with real values
Covering numbers and uniform convergence
The pseudo-dimension and fat-shattering dimension
Bounding covering numbers with dimensions
The sample complexity of classification learning
The dimensions of neural networks
Model selection
Learning Real-Valued Functions:
Learning classes of real functions
Uniform convergence results for real function classes
Bounding covering numbers
The sample complexity of learning function classes
Convex classes
Other learning problems
Algorithmics:
Efficient learning
Learning as optimisation
The Boolean perceptron
Hardness results for feed-forward networks
Constructive learning algorithms for two-layered networks
Table of Contents provided by Publisher. All Rights Reserved.

Neural Network Learning

Theoretical Foundations

At a Glance

Paperback

Industry Reviews

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Hardcover

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