Introduction to Pattern Recognition

by Theodoridis; Pikrakis; Koutroumbas; Cavouras

• ISBN13: 9780123744869
• ISBN10: 0123744865
• Format: Paperback
• Copyright: 2010-03-17
• Publisher: Elsevier Science

Summary

Matlab booklet to accompany Theodoridis, Pattern Recognition 4e. Contains tutorials, examples, and Matlab code corresponding to chapters from the Pattern Recognition text.

Author Biography

Sergios Theodoridis received a degree in Physics from the University of Athens and an MSc and a PhD degree from the Department of Electronics and Electrical Engineering, University of Birmingham, UK. Since 1995, he has been a Professor with the Department of Informatics and Communications at the University of Athens. He has co-authored four papers that have received best paper awards, including the IEEE Computational Intelligence Society Transactions on Neural Networks Outstanding Paper Award. He is a Fellow of IET, a Corresponding Fellow of RSE, and a Fellow of IEEE. Aggelos Pikrakis received a degree in Computer Engineering and Informatics from the University of Patra Greece and a PhD degree in Computer Science from the University of Athens. Since 2007, he has been a Lecturer with the Department of Informatics at the University of Piraeus Greece. Konstantinos Koutroumbas received a degree from the University of Patras, Greece, in Computer Engineering and Informatics, an MSc in Computer Science from the University of London, UK, and a PhD degree from the University of Athens. Since 2001, he has been with the Institute for Space Applications and Remote Sensing of the National Observatory of Athens. Dionisis Cavouras received a BSc in Electronics Engineering and MSc and PhD degrees in Systems Engineering from City University London, UK. Since 1991, he has been a Professor of medical imaging processing at the Department of Medical Instrumentation Technology at the Technological Educational Institute of Athens.

Table of Contents

Preface	p. ix
Classifiers Based on Bayes Decision Theory	p. 1
Introduction	p. 1
Bayes Decision Theory	p. 1
The Gaussian Probability Density Function	p. 2
Minimum Distance Classifiers	p. 6
The Euclidean Distance Classifier	p. 6
The Mahalanobis Distance Classifier	p. 6
Maximum Likelihood Parameter Estimation of Gaussian pdfs	p. 7
Mixture Models	p. 11
The Expectation-Maximization Algorithm	p. 13
Parzen Windows	p. 19
k-Nearest Neighbor Density Estimation	p. 21
The Naive Bayes Classifier	p. 22
The Nearest Neighbor Rule	p. 25
Classifiers Based on Cost Function Optimization	p. 29
Introduction	p. 29
The Perceptron Algorithm	p. 30
The Online Form of the Perceptron Algorithm	p. 33
The Sum of Error Squares Classifier	p. 35
The Multiclass LS Classifier	p. 39
Support Vector Machines: The Linear Case	p. 43
Multiclass Generalizations	p. 48
SVM: The Nonlinear Case	p. 50
The Kernel Perceptron Algorithm	p. 58
The AdaBoost Algorithm	p. 63
Multilayer Perceptrons	p. 66
Data Transformation: Feature Generation and Dimensionality Reduction	p. 79
Introduction	p. 79
Principal Component Analysis	p. 79
The Singular Value Decomposition Method	p. 84
Fisher's Linear Discriminant Analysis	p. 87
The Kernel PCA	p. 92
Laplacian Eigenmap	p. 101
Feature Selection	p. 107
Introduction	p. 107
Outlier Removal	p. 107
Data Normalization	p. 108
Hypothesis Testing: The t-Test	p. 111
The Receiver Operating Characteristic Curve	p. 113
Fisher's Discriminant Ratio	p. 114
Class Separability Measures	p. 117
Divergence	p. 118
Bhattacharyya Distance and Chernoff Bound	p. 119
Measures Based on Scatter Matrices	p. 120
Feature Subset Selection	p. 122
Scalar Feature Selection	p. 123
Feature Vector Selection	p. 124
Template Matching	p. 137
Introduction	p. 137
The Edit Distance	p. 137
Matching Sequences of Real Numbers	p. 139
Dynamic Time Warping in Speech Recognition	p. 143
Hidden Markov Models	p. 147
Introduction	p. 147
Modeling	p. 147
Recognition and Training	p. 148
Clustering	p. 159
Introduction	p. 159
Basic Concepts and Definitions	p. 159
Clustering Algorithms	p. 160
Sequential Algorithms	p. 161
BSAS Algorithm	p. 161
Clustering Refinement	p. 162
Cost Function Optimization Clustering Algorithms	p. 168
Hard Clustering Algorithms	p. 168
Nonhard Clustering Algorithms	p. 184
Miscellaneous Clustering Algorithms	p. 189
Hierarchical Clustering Algorithms	p. 198
Generalized Agglomerative Scheme	p. 199
Specific Agglomerative Clustering Algorithms	p. 200
Choosing the Best Clustering	p. 203
Appendix	p. 209
References	p. 215
Index	p. 217
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