Pattern Classification [Hardcover]

The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.Bayesian Decision Theory.

Maximum-Likelihood and Bayesian Parameter Estimation.

Nonparametric Techniques.

Linear Discriminant Functions.

Multilayer Neural Networks.

Stochastic Methods.

Nonmetric Methods.

Algorithm-Independent Machine Learning.

Unsupervised Learning and Clustering.

Appendix.

Index. ...it provides a good introduction to the subject of Pattern Classification. (Journal of Classification, September 2007)

...a fantastic book! The presentation...could not be better, and I recommend that future authors consider...this book as a role model. (Journal of Statistical Computation and Simulation, March 2006)

...strongly recommended both as a professional reference and as a text for students... (Technometrics, February 2002)

...provides information needed to choose the most appropriate of the many available technique for a given class of problems. (SciTech Book News, Vol. 25, No. 2, June 2001)

I do not believe anybody wishing to teach or do serious work on Pattern Recognition can ignore this book, as it is the sort of book one wishes to find the time to read from cover to cover! (Pattern Analysis & Applications Journal, 2001)

This book is the unique text/professional reference for any serious student or worker in the field olĂ&