Introduction to Pattern Recognition: A Matlab Approach

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A complementary book to Theodoridis and Koutroumbas's Pattern Recognition, Fourth Edition, this book offers self-contained MATLAB® code for the most common methods and algorithms in pattern recognition. In addition, it provides descriptive summaries of the related techniques and algorithms and many solved examples.

Key Features

Complete and tested MATLAB® code for the most common methods and algorithms in feature generation, feature selection, classification, and clustering

Concise descriptions of concepts, methods, and algorithms in pattern recognition

Solved MATLAB® examples, including some real-life data sets in imaging and audio recognition

Available for course use at a special package price with Theodoridis and Koutroumbas's Pattern Recognition, 4e (ISBN for package: 978-0-12-374491-3). For details visit this book's Web site at

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Product Details

  • ISBN-13: 9780123744869
  • Publisher: Elsevier Science
  • Publication date: 3/31/2010
  • Edition description: New Edition
  • Pages: 240
  • Product dimensions: 7.74 (w) x 11.08 (h) x 0.35 (d)

Meet the Author

Sergios Theodoridis is Professor of Signal Processing and Machine Learning in the Department of Informatics and Telecommunications of the University of Athens.
He is the co-author of the bestselling book, Pattern Recognition, and the co-author of Introduction to Pattern Recognition: A MATLAB Approach.
He serves as Editor-in-Chief for the IEEE Transactions on Signal Processing, and he is the co-Editor in Chief with Rama Chellapa for the Academic
Press Library in Signal Processing.

He has received a number of awards including the 2014 IEEE Signal Processing Magazine Best Paper Award, the 2009 IEEE Computational Intelligence Society Transactions on Neural Networks Outstanding Paper Award, the 2014 IEEE Signal Processing Society Education Award, the EURASIP 2014 Meritorious Service Award, and he has served as a Distinguished Lecturer for the IEEE Signal Processing Society and the IEEE Circuits and Systems Society. He is a Fellow of EURASIP and a Fellow of IEEE.

Aggelos Pikrakis is a Lecturer in the Department of Informatics at the University of Piraeus. His research interests stem from the fields of pattern recognition, audio and image processing, and music information retrieval. He is also the co-author of Introduction to Pattern Recognition: A MATLAB Approach (Academic Press, 2010).

Konstantinos Koutroumbas acquired 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 Sensing of the National Observatory of Athens.

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Table of Contents

Preface ix

Chapter 1 Classifiers Based on Bayes Decision Theory 1

1.1 Introduction 1

1.2 Bayes Decision Theory 1

1.3 The Gaussian Probability Density Function 2

1.4 Minimum Distance Classifiers 6

1.4.1 The Euclidean Distance Classifier 6

1.4.2 The Mahalanobis Distance Classifier 6

1.4.3 Maximum Likelihood Parameter Estimation of Gaussian pdfs 7

1.5 Mixture Models 11

1.6 The Expectation-Maximization Algorithm 13

1.7 Parzen Windows 19

1.8 k-Nearest Neighbor Density Estimation 21

1.9 The Naive Bayes Classifier 22

1.10 The Nearest Neighbor Rule 25

Chapter 2 Classifiers Based on Cost Function Optimization 29

2.1 Introduction 29

2.2 The Perceptron Algorithm 30

2.2.1 The Online Form of the Perceptron Algorithm 33

2.3 The Sum of Error Squares Classifier 35

2.3.1 The Multiclass LS Classifier 39

2.4 Support Vector Machines: The Linear Case 43

2.4.1 Multiclass Generalizations 48

2.5 SVM: The Nonlinear Case 50

2.6 The Kernel Perceptron Algorithm 58

2.7 The AdaBoost Algorithm 63

2.8 Multilayer Perceptrons 66

Chapter 3 Data Transformation: Feature Generation and Dimensionality Reduction 79

3.1 Introduction 79

3.2 Principal Component Analysis 79

3.3 The Singular Value Decomposition Method 84

3.4 Fisher's Linear Discriminant Analysis 87

3.5 The Kernel PCA 92

3.6 Laplacian Eigenmap 101

Chapter 4 Feature Selection 107

4.1 Introduction 107

4.2 Outlier Removal 107

4.3 Data Normalization 108

4.4 Hypothesis Testing: The t-Test 111

4.5 The Receiver Operating Characteristic Curve 113

4.6 Fisher's Discriminant Ratio 114

4.7 Class Separability Measures 117

4.7.1 Divergence 118

4.7.2 Bhattacharyya Distance and Chernoff Bound 119

4.7.3 Measures Based on Scatter Matrices 120

4.8 Feature Subset Selection 122

4.8.1 Scalar Feature Selection 123

4.8.2 Feature Vector Selection 124

Chapter 5 Template Matching 137

5.1 Introduction 137

5.2 The Edit Distance 137

5.3 Matching Sequences of Real Numbers 139

5.4 Dynamic Time Warping in Speech Recognition 143

Chapter 6 Hidden Markov Models 147

6.1 Introduction 147

6.2 Modeling 147

6.3 Recognition and Training 148

Chapter 7 Clustering 159

7.1 Introduction 159

7.2 Basic Concepts and Definitions 159

7.3 Clustering Algorithms 160

7.4 Sequential Algorithms 161

7.4.1 BSAS Algorithm 161

7.4.2 Clustering Refinement 162

7.5 Cost Function Optimization Clustering Algorithms 168

7.5.1 Hard Clustering Algorithms 168

7.5.2 Nonhard Clustering Algorithms 184

7.6 Miscellaneous Clustering Algorithms 189

7.7 Hierarchical Clustering Algorithms 198

7.7.1 Generalized Agglomerative Scheme 199

7.7.2 Specific Agglomerative Clustering Algorithms 200

7.7.3 Choosing the Best Clustering 203

Appendix 209

References 215

Index 217

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