Statistical Pattern Recognition

This book provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. It provides a valuable link between the application areas - such as database design, artificial neural networks, and decision support - and the more diverse theoretical topics available to the practitioner or researcher. Each section concludes with a description of the wide range of practical applications that have been addressed and the further developments of theoretical techniques.

Autorentext
Andrew Webb, PhD, is a faculty member in the Department of Electrical and Computer Engineering and the Beckman Institute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign. Dr. Webb has contributed to many areas of magnetic resonance imaging including developments in radiofrequency coil design, feedback control of thermal processes, techniques for localized spectroscopy, and functional brain mapping. He was awarded a Whitaker Foundation Research Award and a National Science Foundation Career Award in 1997, a Wolfgang-Paul Prize from the Alexander von Humbolt Foundation in 2001, and Xerox and Willett awards for young faculty in 2002. He is a Senior Member of the IEEE.

Inhalt
Preface. Notation. 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. Appendices. References. Index.