Statistical pattern recognition / Andrew R. Webb.

Includes bibliographical references (p. [459]-490) and index.

Introduction to statistical pattern recognition -- Density estimation - parametric -- Density estimation - nonparametric -- Linear discriminant analysis -- Nonlinear discriminant analysis - kernel methods -- Nonlinear discriminant analysis - projection methods -- Tree-based methods -- Performance -- Feature selection and extraction -- Clustering -- Additional topics.

Statistical pattern recognition is a very active area of study and research, which has seen many advances in recent years. New and emerging applications - such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition - require robust and efficient pattern recognition techniques. Statistical decision making and estimation are regarded as fundamental to the study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fully updated with new methods, applications and references. It provides a comprehensive introduction to this vibrant area - with material drawn from engineering, statistics, computer science and the social sciences - and covers many application areas, such as database design, artificial neural networks, and decision support systems. The book is aimed primarily at senior undergraduate and graduate students studying statistical pattern recognition, pattern processing, neural networks, and data mining, in both statistics and engineering departments. It is also an excellent source of reference for technical professionals working in advanced information development environments.

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