Pattern recognition
Author(s)
Bibliographic Information
Pattern recognition
Academic Press, c1999
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. This volume's unifying treatment covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. It includes discussion of the latest techniques in wavelets, wavelet packets, and fractals. This book presents cutting-edge material on neural networks, 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.
Table of Contents
Introduction. Classifiers Based on Bayes Decision Theory. Linear Classifiers. Non Linear Classifiers. Feature Selection. Feature Generation I: Linear Transforms.. Feature Generation II. Template Matching. Context Dependent Classification. System Evaluation. Clustering: Basic Concepts. Clustering Algorithms I: Sequential Algorithms. Clustering Algorithms II: Hierarchical Algorithms. Clustering Algorithms III: Schemes Based on Function Optimization. Clustering Algorithms IV. Cluster Validity. Appendices.
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