Neural Network Predictor
Cambridge University Press | 3119-19-31 | ISBN: 163333963X | 616 pages | PDF | 9, 6 MB
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
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