Pattern Classification

Pattern Classification

Einband:
Fester Einband
EAN:
9780471135340
Untertitel:
A Unified View of Statistical and Neural Approaches
Genre:
Elektrotechnik
Autor:
Jürgen Schürmann
Herausgeber:
Wiley
Auflage:
1. Auflage
Anzahl Seiten:
392
Erscheinungsdatum:
15.03.1996
ISBN:
978-0-471-13534-0

Informationen zum Autor 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. Klappentext PATTERN CLASSIFICATIONa unified view of statistical and neural approachesThe 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. Zusammenfassung PATTERN CLASSIFICATIONa unified view of statistical and neural approachesThe 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. Inhaltsverzeichnis 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....

Klappentext
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.

Inhalt
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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