Bøger af Martin (London School of Economics and Political Science) Anthony

This thorough, yet concise, textbook covers key topics in first- and second-year university courses and includes many examples and exercises with solutions to help students practise and master the relevant methods. Crucially, it fully develops the underlying theory so that students can understand how these methods really work.

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.

An introduction to mathematical modelling in economics and finance for students of both economics and mathematics. Throughout, the stress is firmly on how the mathematics relates to economics, illustrated with copious examples and exercises that will foster depth of understanding.