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 VapnikâChervonenkis 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 VapnikâChervonenkis 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. Language: English