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.
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
Probability and Statistics