This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes techniques for modelling probability density functions, and discusses the properties and relative merits of the multi-layer perceptron and radial basis function network models. It also motivates the use of various forms of error functions, and reviews the principal algorithms for error function minimization. As well as providing a detailed discussion of learning and generalization in neural networks, the book also covers the important topics of data processing, feature extraction, and prior knowledge. The book concludes with an extensive treatment of Bayesian techniques and their applications to neural networks.

Table of Contents

1. Statistical pattern recognition; 2. Probability density estimation; 3. Single-layer networks; 4. The multi-layer perceptron; 5. Radial basis functions; 6. Error functions; 7. Parameter optimization algorithms; 8. Pre-processing and feature extraction; 9. Learning and generalization; 10. Bayesian techniques

Reviews

"Should be in the library of any student, teacher, or researcher with a keen interest in modern statistical methods, a large volume of meaningful data to analyze (including simulations), and a fast workstation with good numerical and graphical capabilities."--Journal of the American StatisticalAssociation

.."..should be warmly welcomed by the neural network and pattern recognition communities. Bishop can be recommended to students and engineers in computer science."--Computer Journal

"An excellent and rigorous treatment of a number of neural network architectures."--Journal of Mathematical Psychology

"Its sequential organization and end-of-chapter exercises make it an ideal mental gymnasium. The author has eschewed biological metaphor and sweeping statements in favour of welcome mathematical rigour."--Scientific Computing World

"A first-class book for the researcher in statistical pattern recognition."--Times Higher Education Supplement

"Although there