Pattern Recognition

Sergios Theodoridis, Konstantinos Koutroumbas0126858756, 9780126858754

Table of contents :
Cover……Page 2
Half Title Page……Page 5
Title Page……Page 7
Copyright……Page 8
Table of Contents……Page 9
Preface……Page 17
1.1 Is Pattern Recognition Important?……Page 19
1.2 Features, Feature Vectors, and Classifiers……Page 21
1.3 Supervised versus Unsupervised Pattern Recognition……Page 24
1.4 Outline of the Book……Page 26
2.2 Bayes Decision Theory……Page 31
2.3 Discriminant Functions and Decision Surfaces……Page 37
2.4 Bayesian Classification for Normal Distributions……Page 38
2.5 Estimation of Unknown Probability Density Functions……Page 45
2.6 The Nearest Neighbor Rule……Page 62
3.2 Linear Discriminant Functions and Decision Hyperplanes……Page 73
3.3 The Perceptron Algorithm……Page 75
3.4 Least Squares Methods……Page 83
3.5 Mean Square Estimation Revisited……Page 90
3.6 Support Vector Machines……Page 95
4.2 The XOR Problem……Page 111
4.3 The Two-Layer Perceptron……Page 112
4.4 Three-Layer Perceptrons……Page 119
4.5 Algorithms Based on Exact Classification of the Training Set……Page 120
4.6 The Backpropagation Algorithm……Page 122
4.7 Variations on the Backpropagation Theme……Page 130
4.8 The Cost Function Choice……Page 133
4.9 Choice of the Network Size……Page 136
4.10 A Simulation Example……Page 142
4.11 Networks with Weight Sharing……Page 144
4.12 Generalized Linear Classifiers……Page 145
4.13 Capacity of the l-Dimensional Space in Linear Dichotomies……Page 147
4.14 Polynomial Classifiers……Page 149
4.15 Radial Basis Function Networks……Page 151
4.16 Universal Approximators……Page 155
4.17 Support Vector Machines: The Nonlinear Case……Page 157
4.18 Decision Trees……Page 161
4.19 Discussion……Page 168
5.1 Introduction……Page 181
5.2 Preprocessing……Page 182
5.3 Feature Selection Based on Statistical Hypothesis Testing……Page 184
5.4 The Receiver Operating Characteristics CROC Curve……Page 191
5.5 Class Separability Measures……Page 192
5.6 Feature Subset Selection……Page 199
5.7 Optimal Feature Generation……Page 205
5.8 Neural Networks and Feature Generation/Selection……Page 209
5.9 A Hint on the Vapnik-Chernovenkis Learning Theory……Page 211
6.1 Introduction……Page 225
6.2 Basis Vectors and Images……Page 226
Local Disk……Page 0
6.4 The Singular Value Decomposition……Page 233
6.5 Independent Component Analysis……Page 237
6.6 The Discrete Fourier Transform (DFT)……Page 244
6.7 The Discrete Cosine and Sine Transforms……Page 248
6.8 The Hadamard Transform……Page 249
6.9 The Haar Transform……Page 251
6.10 The Haar Expansion Revisited……Page 253
6.11 Discrete Time Wavelet Transform (DTWT)……Page 257
6.12 The Multiresolution Interpretation……Page 267
6.14 A Look at Two-Dimensional Generalizations……Page 270
6.15 Applications……Page 273
7.1 Introduction……Page 287
7.2 Regional Features……Page 288
7.3 Features for Shape and Size Characterization……Page 312
7.4 A Glimpse at Fractals……Page 321
8.1 Introduction……Page 339
8.2 Measures Based on Optimal Path Searching Techniques……Page 340
8.3 Measures Based on Correlations……Page 355
8.4 Deformable Template Models……Page 361
9.2 The Bayes Classifier……Page 369
9.3 Markov Chain Models……Page 370
9.4 The Viterbi Algorithm……Page 371
9.5 Channel Equalization……Page 374
9.6 Hidden Markov Models……Page 379
9.7 Training Markov Models via Neural Networks……Page 391
9.8 A Discussion of Markov Random Fields……Page 393
10.2 Error Counting Approach……Page 403
10.3 Exploiting the Finite Size of the Data Set……Page 405
10.4 A Case Study from Medical Imaging……Page 408
11.1 Introduction……Page 415
11.2 Proximity Measures……Page 422
12.1 Introduction……Page 447
12.2 Categories of Clustering Algorithms……Page 449
12.3 Sequential Clustering Algorithms……Page 451
12.4 A Modification of BSAS……Page 455
12.5 A Two-Threshold Sequential Scheme……Page 456
12.6 Refinement Stages……Page 459
12.7 Neural Network Implementation……Page 461
13.1 Introduction……Page 467
13.2 Agglomerative Algorithms……Page 468
13.3 The Cophenetic Matrix……Page 494
13.4 Divisive Algorithms……Page 496
13.5 Choice of the Best Number of Clusters……Page 498
14.1 Introduction……Page 507
14.2 Mixture Decomposition Schemes……Page 509
14.3 Fuzzy Clustering Algorithms……Page 518
14.4 Possibilistic Clustering……Page 540
14.5 Hard Clustering Algorithms……Page 547
14.6 Vector Quantization……Page 551
15.2 Clustering Algorithms Based on Graph Theory……Page 563
15.3 Competitive Learning Algorithms……Page 570
15.4 Branch and Bound Clustering Algorithms……Page 579
15.5 Binary Morphology Clustering Algorithms (BMCAs)……Page 582
15.6 Boundary Detection Algorithms……Page 591
15.7 Valley-Seeking Clustering Algorithms……Page 594
15.8 Clustering via Cost Optimization (Revisited)……Page 596
15.9 Clustering Using Genetic Algorithms……Page 600
15.10 Other Clustering Algorithms……Page 601
16.1 Introduction……Page 609
16.2 Hypothesis Testing Revisited……Page 610
16.3 Hypothesis Testing in Cluster Validity……Page 612
16.4 Relative Criteria……Page 623
16.5 Validity of Individual Clusters……Page 639
16.6 Clustering Tendency……Page 642
Appendix A: Hints from Probability and Statistics……Page 661
Appendix B: Linear Algebra Basics……Page 673
Appendix C: Cost Function Optimization……Page 677
Appendix D: Basic Definitions from Linear Systems Theory……Page 695
Index……Page 699
Back Cover……Page 711
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