Pattern Recognition

Other | May 1, 2003

byTheodoridis, Sergios, Sergios Theodoridis, Konstantinos Koutroumbas

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Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. Patter Recognition, 2e covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. This book presents cutting-edge material on neural networks, - a set of linked microprocessors that can form associations and uses pattern recognition to "learn" -and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct result of more than 10 years of teaching experience, the text was developed by the authors through use in their own classrooms.

*Approaches pattern recognition from the designer's point of view
*New edition highlights latest developments in this growing field, including independent components and support vector machines, not available elsewhere
*Supplemented by computer examples selected from applications of interest

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From the Publisher

Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. Patter Recognition, 2e covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. This book presents cutting-edge mate...

Konstantinos Koutroumbasacquired a degree from the University of Patras, Greece in Computer Engineering and Informatics in 1989, a MSc in Computer Science from the University of London, UK in 1990, and a Ph.D. degree from the University of Athens in 1995. Since 2001 he has been with the Institute for Space Applications and Remote Sensi...

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Format:OtherDimensions:689 pages, 1 × 1 × 1 inPublished:May 1, 2003Publisher:Academic PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:008051362X

ISBN - 13:9780080513621

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Table of Contents

Classifiers Based on Bayes Decision Theory; Support Vector machines (linear case); Support Vector Machines(nonlinear case); Decision Trees; Linear Classifiers Non Linear Classifiers; Deformable Template Matching; Feature Selection Feature Generation I: Linear Transforms; Feature Generation II. Template Matching; Context Dependent Classification; System Evaluation Clustering: Basic Concepts; Clustering AlgorithmsI: Sequential Algorithms; Clustering AlgorithmsII: Hierarchical Algorithms; Clustering AlgorithmsIII: Schemes Based on Function Optimization; Clustering AlgorithmsIV; Cluster Validity