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Pattern Classification: A Unified View of Statistical and Neural Approaches
 
 
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Pattern Classification: A Unified View of Statistical and Neural Approaches [Hardcover]

Jürgen Schürmann (Author)

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Book Description

March 1996 0471135348 978-0471135340 1
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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Editorial Reviews

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.

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Inside This Book (learn more)
First Sentence:
The ability of pattern classification is certainly one of the key features of intelligent behavior, be it of humans or animals. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
optimum coefficient matrix, posteriori probabilities prob, learning sample set, recursive learning rule, confidence mapping function, multilayer perceptron approach, priori probability prob, pairwise borders, classifier adaptation, given pattern source, garbage patterns, soft labeling, perceptron regression, sigmoidal mapping, regression classifier, hard labeling, probability function prob, pattern classification system, functional classifier, given learning set, pairwise classifiers, optimum classifier, perceptron layer, posteriori probability functions, polynomial discriminant functions
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