Introduction to Pattern Recognition: A MATLAB Approach

✍ Scribed by Theodoridis, Sergios

• Publisher: Academic Press
• Year: 2010;2009
• Tongue: English
• Leaves: 233
• Edition: 4th ed
• Category: Library

✦ Synopsis

Introduction to Pattern Recognition: A Matlab Approachis an accompanying manual to Theodoridis/Koutroumbas' Pattern Recognition.

It includes Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition.

This text is designed for electronic engineering, computer science, computer engineering, biomedical engineering and applied mathematics students taking graduate courses on pattern recognition and machine learning as well as R&D engineers and university researchers in image and signal processing/analyisis, and computer vision.

✦ Table of Contents

Front Cover......Page 1
Title Page......Page 4
Copyright Page......Page 5
Table of Contents......Page 6
Preface......Page 10
1.2 Bayes Decision Theory......Page 14
1.3 The Gaussian Probability Density Function......Page 15
1.4.2 The Mahalanobis Distance Classifier......Page 19
1.4.3 Maximum Likelihood Parameter Estimation of Gaussian pdfs......Page 20
1.5 Mixture Models......Page 24
1.6 The Expectation-Maximization Algorithm......Page 26
1.7 Parzen Windows......Page 32
1.8 k-Nearest Neighbor Density Estimation......Page 34
1.9 The Naive Bayes Classifier......Page 35
1.10 The Nearest Neighbor Rule......Page 38
2.1 Introduction......Page 42
2.2 The Perceptron Algorithm......Page 43
2.2.1 The Online Form of the Perceptron Algorithm......Page 46
2.3 The Sum of Error Squares Classifier......Page 48
2.3.1 The Multiclass LS Classifier......Page 52
2.4 Support Vector Machines: The Linear Case......Page 56
2.4.1 Multiclass Generalizations......Page 61
2.5 SVM: The Nonlinear Case......Page 63
2.6 The Kernel Perceptron Algorithm......Page 71
2.7 The AdaBoost Algorithm......Page 76
2.8 Multilayer Perceptrons......Page 79
3.2 Principal Component Analysis......Page 92
3.3 The Singular Value Decomposition Method......Page 97
3.4 Fisher’s Linear Discriminant Analysis......Page 100
3.5 The Kernel PCA......Page 105
3.6 Laplacian Eigenmap......Page 114
4.2 Outlier Removal......Page 120
4.3 Data Normalization......Page 121
4.4 Hypothesis Testing: The t-Test......Page 124
4.5 The Receiver Operating Characteristic Curve......Page 126
4.6 Fisher’s Discriminant Ratio......Page 127
4.7 Class Separability Measures......Page 130
4.7.1 Divergence......Page 131
4.7.2 Bhattacharyya Distance and Chernoff Bound......Page 132
4.7.3 Measures Based on Scatter Matrices......Page 133
4.8 Feature Subset Selection......Page 135
4.8.1 Scalar Feature Selection......Page 136
4.8.2 Feature Vector Selection......Page 137
5.2 The Edit Distance......Page 150
5.3 Matching Sequences of Real Numbers......Page 152
5.4 Dynamic Time Warping in Speech Recognition......Page 156
6.2 Modeling......Page 160
6.3 Recognition and Training......Page 161
7.2 Basic Concepts and Definitions......Page 172
7.3 Clustering Algorithms......Page 173
7.4.1 BSAS Algorithm......Page 174
7.4.2 Clustering Refinement......Page 175
7.5.1 Hard Clustering Algorithms......Page 181
7.5.2 Nonhard Clustering Algorithms......Page 197
7.6 Miscellaneous Clustering Algorithms......Page 202
7.7 Hierarchical Clustering Algorithms......Page 211
7.7.1 Generalized Agglomerative Scheme......Page 212
7.7.2 Specific Agglomerative Clustering Algorithms......Page 213
7.7.3 Choosing the Best Clustering......Page 216
Appendix......Page 222
References......Page 228
Index......Page 230