Neural Network Learning

First published in 1999, this book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised lea... » 

First published in 1999, this 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 Vapnik-Chervonenkis 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 Vapnik-Chervonenkis dimension in large margin classification, and in real prediction. They 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 is intended to be accessible to researchers and graduate students in computer science, engineering, and mathematics.Contains results that have not appeared in journal papers or other booksPresents many recent results in a unified framework and, in many cases, with simpler proofsSelf-contained: it introduces the necessary background material on probability, statistics, combinatorics and computational complexityIt is suitable for graduate students as well as active researchers in the area (parts of it have already formed the basis of a graduate course) «