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Pattern recognition and neural networks. (English) Zbl 0853.62046
Cambridge: Cambridge Univ. Press. xi, 403 p. (1996).
The author provides an in-depth study of methods for pattern recognition drawn from engineering, statistics, machine learning and neural networks. All the modern branches of the subject are covered together with case studies and applications. The relevant parts of statistical decision theory and computational learning theory are included, as well as methods such as feed-forward neural networks, radial basis functions, learning vector quantization and Kohonen’s self-organizing maps. All the principal results are proved, the proofs being short and often original. The methods are illustrated on real examples, the data for which are available on the Internet.
The formal pre-requisites to follow this book are rather few: a background in linear algebra, a knowledge of calulus and its use in finding extrema, and an introductory course in probability and statistics. This book can serve both as an introduction for graduate students and non-specialist readers, and also as a standard reference for the more expert reader.
Chapter headings: (1) Introduction and Examples; (2) Statistical Decision Theory; (3) Linear Discriminant Analysis; (4) Flexible Discriminants; (5) Feed-forward Neural Networks; (6) Nonparametric Methods; (7) Tree-structured Classifiers; (8) Belief Networks; (9) Unsupervised Methods; (10) Finding Good Pattern Features.

MSC:
62H30 Classification and discrimination; cluster analysis (statistical aspects)
68T10 Pattern recognition, speech recognition
62-01 Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics
68T05 Learning and adaptive systems in artificial intelligence
68-01 Introductory exposition (textbooks, tutorial papers, etc.) pertaining to computer science
62C05 General considerations in statistical decision theory
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