Pattern Classification

From the reviews of the First Edition . . .

"The first edition of this book, published 30 years ago by Duda and Hart, has been a defining book for the field of Pattern Recognition. Stork has done a superb job of updating the book. He has undertaken a monumental task of sifting through 30 years of material in a rapidly growing field and presented another snapshot of the field, determining what will be of importance for the next 30 years and incorporating it into this second edition. The style is easy to read as in the original book and the statistical, mathematical material comes alive with many new illustrations. The end result is harmonious, leading the reader through many new topics. . ." --Sargur N. Srihari, PhD, Director, Center for Excellence in Document Analysis and Recognition, Distinguished Professor, Department of Computer Science and Engineering, SUNY at Buffalo

Practitioners developing or investigating pattern recognition systems in such diverse application areas as speech recognition, optical character recognition, image processing, or signal analysis, often face the difficult task of having to decide among a bewildering array of available techniques. This unique text/professional reference provides the information you need to choose the most appropriate method for a given class of problems, presenting an in-depth, systematic account of the major topics in pattern recognition today. A new edition of a classic work that helped define the field for over a quarter century, this practical book updates and expands the original work, focusing on pattern classification and the immense progress it has experienced in recent years. Special features include:
* Clear explanations of both classical and new methods, including neural networks, stochastic methods, genetic algorithms, and theory of learning
* Over 350 high-quality, two-color illustrations highlighting various concepts
* Numerous worked examples
* Pseudocode for pattern recognition algorithms
* Expanded problems, keyed specifically to the text
* Complete exercises, linked to the text
* Algorithms to explain specific pattern-recognition and learning techniques
* Historical remarks and important references at the end of chapters
* Appendices covering the necessary mathematical background

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.