Neural Networks for Pattern Recognition is a comprehensive book for postgraduate students of computer science, statistics or mathematics. The book explores the concept of neural networks, discussing feed-forward networks from the perspective of statistical pattern recognition. It has been designed for a single semester course in neural networks, and helps readers understand techniques for modelling probability density functions, the properties and relative merits of the multi-layer perceptron and radial basis function network models. It also helps readers make better use of various forms of error function and explores the principal algorithms for error function minimization. In addition, the book contains a detailed discussion of inneural networks, data processing, feature extraction, and the application of Bayesian techniques for neural networks. The book is an indispensable resource for all postgraduates studying advanced statistics and pattern recognition.
About Christopher M. Bishop
Christopher M. Bishop is an English scientist, best associated with Microsoft Research Ltd in Cambridge, where he heads the Machine Learning and Perception group. Also a Chair of Computer Science at the University of Edinburgh, he studies machine learning, neural networks, pattern recognition, and natural language processing and applications.
A graduate of St Catherine's College, Oxford, Dr. Bishop obtained a doctoral degree in Theoretical Physics at the University of Edinburgh and worked at the Culham Centre for Fusion Energy as a research scientist. He also worked for a brief period as a Professor of Computer Science at Aston University.