Ebook: Statistical Pattern Recognition, Third Edition

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.

Statistical Pattern Recognition, 3^rd Edition:

• Provides a self-contained introduction to statistical pattern recognition.
• Includes new material presenting the analysis of complex networks.
• Introduces readers to methods for Bayesian density estimation.
• Presents descriptions of new applications in biometrics, security, finance and condition monitoring.
• Provides descriptions and guidance for implementing techniques, which will be invaluable to software engineers and developers seeking to develop real applications
• Describes mathematically the range of statistical pattern recognition techniques.
• Presents a variety of exercises including more extensive computer projects.

The in-depth technical descriptions make the book suitable for senior undergraduate and graduate students in statistics, computer science and engineering.  Statistical Pattern Recognition is also an excellent reference source for technical professionals.  Chapters have been arranged to facilitate implementation of the techniques by software engineers and developers in non-statistical engineering fields.

www.wiley.com/go/statistical_pattern_recognitionContent:
Chapter 1 Introduction to Statistical Pattern Recognition (pages 1–32):
Chapter 2 Density Estimation – Parametric (pages 33–69):
Chapter 3 Density Estimation – Bayesian (pages 70–149):
Chapter 4 Density Estimation – Nonparametric (pages 150–220):
Chapter 5 Linear Discriminant Analysis (pages 221–273):
Chapter 6 Nonlinear Discriminant Analysis – Kernel and Projection Methods (pages 274–321):
Chapter 7 Rule and Decision Tree Induction (pages 322–360):
Chapter 8 Ensemble Methods (pages 361–403):
Chapter 9 Performance Assessment (pages 404–432):
Chapter 10 Feature Selection and Extraction (pages 433–500):
Chapter 11 Clustering (pages 501–554):
Chapter 12 Complex Networks (pages 555–580):
Chapter 13 Additional Topics (pages 581–590):