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PATTERN CLASSIFICATION

a unified view of statistical and neural approaches

The product of years of research and practical experience in pattern classification, this book offers a theory-based engineering perspective on neural networks and statistical pattern classification. Pattern Classification sheds new light on the relationship between seemingly unrelated approaches to pattern recognition, including statistical methods, polynomial regression, multilayer perceptron, and radial basis functions. Important topics such as feature selection, reject criteria, classifier performance measurement, and classifier combinations are fully covered, as well as material on techniques that, until now, would have required an extensive literature search to locate. A full program of illustrations, graphs, and examples helps make the operations and general properties of different classification approaches intuitively understandable.

Offering a lucid presentation of complex applications and their algorithms, Pattern Classification is an invaluable resource for researchers, engineers, and graduate students in this rapidly developing field.

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Statistical Decision Theory.
 
 Need for Approximations: Fundamental Approaches.
 
 Classification Based on Statistical Models Determined by First-and-Second Order Statistical Moments.
 
 Classification Based on Mean-Square Functional Approximations.
 
 Polynomial Regression.
 
 Multilayer Perceptron Regression.
 
 Radial Basis Functions.
 
 Measurements, Features, and Feature Section.
 
 Reject Criteria and Classifier Performance.
 
 Combining Classifiers.
 
 Conclusion.
 
 STATMOD Program: Description of ftp Package.
 
 References.
 
 Index.

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