642 pages, b/w illustrations
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. Statistical Pattern Recognition 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, 3rd 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 Statistical Pattern Recognition 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.
"In the end I must add that this book is so appealing that I often found myself lost in the reading, pausing the overview of the manuscript in order to look more into some presented subject, and not being able to continue until I had finished seeing all about it."
- Zentralblatt MATH, 1 December 2012
1 Introduction to Statistical Pattern Recognition 1
2 Density Estimation - Parametric 33
3 Density Estimation - Bayesian 70
4 Density Estimation - Nonparametric 150
5 Linear Discriminant Analysis 221
6 Nonlinear Discriminant Analysis - Kernel and Projection Methods 274
7 Rule and Decision Tree Induction 322
8 Ensemble Methods 361
9 Performance Assessment 404
10 Feature Selection and Extraction 433
11 Clustering 501
12 Complex Networks 555
13 Additional Topics 581
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