Statistical Pattern Recognition, Second Edition (Repost)

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. * Provides a self-contained introduction to statistical pattern recognition. * Each technique described is illustrated by real examples. * Covers Bayesian methods, neural networks, support vector machines, and unsupervised classification. * Each section concludes with a description of the applications that have been addressed and with further developments of the theory. * Includes background material on dissimilarity, parameter estimation, data, linear algebra and probability. * Features a variety of exercises, from 'open-book' questions to more lengthy projects. 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. For further information on the techniques and applications discussed in this book please visit www.statistical-pattern-recognition.netContent: Chapter 1 Introduction to Statistical Pattern Recognition (pages 1–31): Chapter 2 Density Estimation – Parametric (pages 33–80): Chapter 3 Density Estimation – Nonparametric (pages 81–122): Chapter 4 Linear Discriminant Analysis (pages 123–168): Chapter 5 Nonlinear Discriminant Analysis – Kernel Methods (pages 169–202): Chapter 6 Nonlinear Discriminant Analysis – Projection Methods (pages 203–224): Chapter 7 Tree?Based Methods (pages 225–249): Chapter 8 Performance (pages 251–303): Chapter 9 Feature Selection and Extraction (pages 305–360): Chapter 10 Clustering (pages 361–407): Chapter 11 Additional Topics (pages 409–418):