Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions. It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques.

　　This third edition 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. The book has been updated to cover new methods and applications, and includes a wide range of techniques such as Bayesian methods, neural networks, support vector machines, feature selection and feature reduction techniques.Technical descriptions and motivations are provided, and the techniques are illustrated using real examples.

Preface
Notation
Ch1: Introduction to Statistical Pattern Recognition
Ch2: Density Estimation – Parametric
Ch3: Density Estimation – Bayesian
Ch4: Density Estimation – Nonparametric
Ch5: Linear Discriminant Analysis
Ch6: Nonlinear Discriminant Analysis – Kernel and Projection Methods
Ch7: Rule and Decision Tree Induction
Ch8: Ensemble Methods
Ch9: Performance Assessment
Ch10: Feature Selection and Extraction
Ch11: Clustering
Ch12: Complex Networks
Ch13: Additional Topics
References
Index