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

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(美)Richard O.Duda Peter E.Hart David G.Stork：Richard O.Duda: 于麻省理工学院获得电气工程博士学位，是加州San Jose州立大学电气工程系名誉教授。他是美国人工智能学会会士、IEEE会士。
Peter E.Hart: 是加州Ricoh Innovations公司的创始人、总裁和CEO，同时还是理光公司的高级副总裁，在此之前曾任理光加州研究中心的高级副总裁。他是美国人工智能学会会士、IEEE会士，曾获IEEE信息论协会50周年论文奖。
David G.Stork: 于马里兰大学获得博士学位，现任加州Ricoh Innovations公司的首席科学家，同时也是斯坦福大学电气工程与计算机科学客座教授。

Preface
1.Introduction
2.Bayesian Decision Theory.
3.Maximum-Likelihood and Bayesian Parameter Estimation.
4.Nonparametric Techniques.
5.Linear Discriminant Functions.
6.Multilayer Neural Networks.
7.Stochastic Methods.
8.Nonmetric Methods.
9.Algorithm-Independent Machine Learning.
10.Unsupervised Learning and Clustering.
Appendix.
Index.