Statistical Pattern Recognition, 2nd Edition
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.net
Introduction to statistical pattern recognition.
Density estimation - parametric.
Density estimation - nonparametric.
Linear discriminant analysis
Nonlinear discriminant analysis - kernel methods.
Nonlinear discriminant analysis - projection methods.
Feature selection and extraction.
- Contains descriptions of the most up-to-date pattern processing techniques.
- Applications emphasis - although there is much mathematical detail, techniques are illustrated by real application studies.
- Breadth of material - the book draws together material from diverse sources including the statistics, engineering, machine learning literatures.
- Includes a variety of exercises from 'open-book' questions to more lengthy projects.
- Updated and expanded coverage, including neural networks, Bayesian networks, machine learning and flexible discriminant analysis.
- New material on feature extraction - wavelets, MCMC techniques, sequential Bayesian analysis (particle filters), self-organising systems, support vector machines, and combining classifiers/multiple models.
- Includes an extensive bibliography.