Pattern classification: a unified view of statistical and neural approaches

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John Wiley & Sons, Mar 15, 1996 - Computers - 373 pages
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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
Fundamental Approaches
Classification Based on Statistical Models Determined

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About the author (1996)

JÜRGEN SCHÜRMANN is head of the Pattern Understanding Group at the Daimler-Benz Research Center in Ulm, Germany, and teaches at the Technical University of Darmstadt.

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