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
|Product dimensions:||8.60(w) x 11.10(h) x 1.50(d)|
Table of Contents
Maximum-Likelihood and Bayesian Parameter Estimation.
Linear Discriminant Functions.
Multilayer Neural Networks.
Algorithm-Independent Machine Learning.
Unsupervised Learning and Clustering.