*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
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. This book presents cutting-edge material on neural networks, - a set of linked microprocessors that can form associations and uses pattern recognition to "learn". 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:
Intro; 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 Algorithms I: Sequential Algorithms; Clustering
Algorithms II: Hierarchical Algorithms; Clustering Algorithms III: Schemes Based
on Function Optimization; Clustering Algorithms IV. Cluster Validity; Appendices.
*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
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. This book presents cutting-edge material on neural networks, - a set of linked microprocessors that can form associations and uses pattern recognition to "learn". 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:
Intro; 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 Algorithms I: Sequential Algorithms; Clustering
Algorithms II: Hierarchical Algorithms; Clustering Algorithms III: Schemes Based
on Function Optimization; Clustering Algorithms IV. Cluster Validity; Appendices.