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Pattern Classification: A Unified View of Statistical and Neural Approaches, by Jürgen Schürmann
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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.
- Sales Rank: #4101351 in Books
- Published on: 1996-03-15
- Original language: English
- Number of items: 1
- Dimensions: 9.55" h x .92" w x 6.38" l, 1.55 pounds
- Binding: Hardcover
- 392 pages
From the Publisher
Based on many years of practical experience predominately in the area of character recognition and document analysis, the author details a number of competing approaches to building up essential estimating functions--statistical modeling, least mean squares techniques and radial basis functions. Traditional statistics-based pattern classification techniques as well as connectionist and neural methods are coherently treated and shown to be inextricably interfused. Uses an extremely simplified two-dimensional example task throughout the text to illustrate diverse approaches in a unified manner.
From the Back Cover
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.
About the Author
J�RGEN SCH�RMANN 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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By Jose I. Miranda
Hi all:
Sch�rmann gives you a good view on the use of multivariate statistics and neural net. I used it mainly for learning the neural net subject.
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