Neural Network Learning Theoretical Foundations Anthony Bartlett Paperback

Neural Network LearningTheoretical Foundations\nAuthor(s): Martin Anthony, Peter L. Bartlett\nFormat: Paperback\nPublisher: Cambridge University Press, United Kingdom\nImprint: Cambridge University Press\nISBN-13: 9780521118620, 978-0521118620\nSynopsis\nThis book describes 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. Research on pattern classification with binary-output networks is surveyed, including a discussion of the relevance of the VapnikChervonenkis dimension, and calculating estimates of the dimension for several neural network models. A model of classification by real-output networks is developed, and the usefulness of classification with a 'large margin' is demonstrated. The authors explain the role of scale-sensitive versions of the VapnikChervonenkis dimension in large margin classification, and in real prediction. They also.