Elsevier S & T

Pattern Recognition

Theodoridis, Sergios; Theodoridis, Sergios; Koutroumbas, Konstantinos; Koutroumbas, Konstantinos

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Table of contents
  • Cover
  • CONTENTSv
  • Prefacexiii
  • Chapter 1. INTRODUCTION1
  • 1.1 Is Pattern Recognition Important?1
  • 1.2 Features, Feature Vectors, and Classifiers3
  • 1.3 Supervised Versus Unsupervised Pattern Recognition6
  • 1.4 Outline of the Book8
  • Chapter 2. CLASSIFIERS BASED ON BAYES DECISION THEORY13
  • 2.1 Introduction13
  • 2.2 Bayes Decision Theory13
  • 2.3 Discriminant Functions and Decision Surfaces19
  • 2.4 Bayesian Classification for Normal Distributions20
  • 2.5 Estimation of Unknown Probability Density Functions27
  • 2.6 The Nearest Neighbor Rule44
  • Chapter 3. LINEAR CLASSIFIERS55
  • 3.1 Introduction55
  • 3.2 Linear Discriminant Functions and Decision Hyperplanes55
  • 3.3 The Perceptron Algorithm57
  • 3.4 Least Squares Methods65
  • 3.5 Mean Square Estimation Revisited72
  • 3.6 Support Vector Machines77
  • Chapter 4. NONLINEAR CLASSIFIERS93
  • 4.1 Introduction93
  • 4.2 The XOR Problem93
  • 4.3 The Two-Layer Perceptron94
  • 4.4 Three-Layer Perceptrons101
  • 4.5 Algorithms Based on Exact Classification of the Training Set102
  • 4.6 The Backpropagation Algorithm104
  • 4.7 Variations on the Backpropagation Theme112
  • 4.8 The Cost Function Choice115
  • 4.9 Choice of the Network Size118
  • 4.10 A Simulation Example124
  • 4.11 NetworksWithWeight Sharing126
  • 4.12 Generalized Linear Classifiers127
  • 4.13 Capacity of the l-Dimensional Space in Linear Dichotomies129
  • 4.14 Polynomial Classifiers131
  • 4.15 Radial Basis Function Networks133
  • 4.16 Universal Approximators137
  • 4.17 Support Vector Machines: The Nonlinear Case139
  • 4.18 Decision Trees143
  • 4.19 Discussion150
  • Chapter 5. FEATURE SELECTION163
  • 5.1 Introduction163
  • 5.2 Preprocessing164
  • 5.3 Feature Selection Based on Statistical Hypothesis Testing166
  • 5.4 The Receiver Operating Characteristics CROC Curve173
  • 5.5 Class Separability Measures174
  • 5.6 Feature Subset Selection181
  • 5.7 Optimal Feature Generation187
  • 5.8 Neural Networks and Feature Generation/Selection191
  • 5.9 A Hint on the Vapnik–Chernovenkis Learning Theory193
  • Chapter 6. FEATURE GENERATION I: LINEAR TRANSFORMS207
  • 6.1 Introduction207
  • 6.2 Basis Vectors and Images208
  • 6.3 The Karhunen–Loève Transform210
  • 6.4 The Singular Value Decomposition215
  • 6.5 Independent Component Analysis219
  • 6.6 The Discrete Fourier Transform (DFT)226
  • 6.7 The Discrete Cosine and Sine Transforms230
  • 6.8 The Hadamard Transform231
  • 6.9 The Haar Transform233
  • 6.10 The Haar Expansion Revisited235
  • 6.11 Discrete TimeWavelet Transform (DTWT)239
  • 6.12 The Multiresolution Interpretation249
  • 6.13 Wavelet Packets252
  • 6.14 A Look at Two-Dimensional Generalizations252
  • 6.15 Applications255
  • Chapter 7. FEATURE GENERATION II269
  • 7.1 Introduction269
  • 7.2 Regional Features270
  • 7.3 Features for Shape and Size Characterization294
  • 7.4 A Glimpse at Fractals303
  • Chapter 8. TEMPLATE MATCHING321
  • 8.1 Introduction321
  • 8.2 Measures Based on Optimal Path Searching Techniques322
  • 8.3 Measures Based on Correlations337
  • 8.4 Deformable Template Models343
  • Chapter 8. TEMPLATE MATCHING321
  • 8.1 Introduction321
  • 8.2 Measures based on optimal path searching techniques322
  • 8.3 Measures based on correlations337
  • 8.4 Deformable template models343
  • Chapter 9. CONTEXT-DEPENDENT CLASSIFICATION351
  • 9.1 Introduction351
  • 9.2 The Bayes Classifier351
  • 9.3 Markov Chain Models352
  • 9.4 The Viterbi Algorithm353
  • 9.5 Channel Equalization356
  • 9.6 Hidden Markov Models361
  • 9.7 Training Markov Models via Neural Networks373
  • 9.8 A discussion of Markov Random Fields375
  • Chapter 10. SYSTEM EVALUATION385
  • 10.1 Introduction385
  • 10.2 Error Counting Approach385
  • 10.3 Exploiting the Finite Size of the Data Set387
  • 10.4 A Case Study From Medical Imaging390
  • Chapter 11. CLUSTERING: BASIC CONCEPTS397
  • 11.1 Introduction397
  • 11.2 Proximity Measures404
  • Chapter 12. CLUSTERING ALGORITHMS I: SEQUENTIAL ALGORITHMS429
  • 12.1 Introduction429
  • 12.2 Categories of Clustering Algorithms431
  • 12.3 Sequential Clustering Algorithms433
  • 12.4 A Modification of BSAS437
  • 12.5 ATwo-Threshold Sequential Scheme438
  • 12.6 Refinement Stages441
  • 12.7 Neural Network Implementation443
  • Chapter 13. CLUSTERING ALGORITHMS II: HIERARCHICAL ALGORITHMS449
  • 13.1 Introduction449
  • 13.2 Agglomerative Algorithms450
  • 13.3 The Cophenetic Matrix476
  • 13.4 Divisive Algorithms478
  • 13.5 Choice of the Best Number of Clusters480
  • Chapter 14. CLUSTERING ALGORITHMS III: SCHEMES BASED ON FUNCTION OPTIMIZATION489
  • 14.1 Introduction489
  • 14.2 Mixture Decomposition Schemes491
  • 14.3 Fuzzy Clustering Algorithms500
  • 14.4 Possibilistic Clustering522
  • 14.5 Hard Clustering Algorithms529
  • 14.6 Vector Quantization533
  • Chapter 15. CLUSTERING ALGORITHMS IV545
  • 15.1 Introduction545
  • 15.2 Clustering Algorithms Based on Graph Theory545
  • 15.3 Competitive Learning Algorithms552
  • 15.4 Branch and Bound Clustering Algorithms561
  • 15.5 Binary Morphology Clustering Algorithms (BMCAs)564
  • 15.6 Boundary Detection Algorithms573
  • 15.7 Valley-Seeking Clustering Algorithms576
  • 15.8 Clustering Via Cost Optimization (Revisited)578
  • 15.9 Clustering Using Genetic Algorithms582
  • 15.10 Other Clustering Algorithms583
  • Chapter 16. CLUSTER VALIDITY591
  • 16.1 Introduction591
  • 16.2 Hypothesis Testing Revisited592
  • 16.3 Hypothesis Testing in Cluster Validity594
  • 16.4 Relative Criteria605
  • 16.5 Validity of Individual Clusters621
  • 16.6 Clustering Tendency624
  • Appendix A: Hints from Probability and Statistics643
  • Appendix B: Linear Algebra Basics655
  • Appendix C: Cost Function Optimization659
  • Appendix D: Basic Definitions from Linear Systems Theory677
  • Index681
Book details
  • Vendor Elsevier S & T
  • SKU 9780126858754
  • ISBN-13 9780080513621
  • Author Theodoridis, Sergios; Theodoridis, Sergios; Koutroumbas, Konstantinos; Koutroumbas, Konstantinos
  • Edition 2nd
  • Category Technology & Engineering
  • Subject Automation

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Description eBook delivery & access Reviews
Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. Patter Recognition, 2e 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" -and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct result of more than 10 years of teaching experience, the text was developed by the authors through use in their own classrooms.

*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