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Bibliographic Details
Main Author:	Theodoridis, Sergios 1951- (Author)
Format:	Book
Language:	English
Published:	Amsterdam [u.a.] Academic Press 2006
Edition:	3. ed.
Subjects:	Reconnaissance des formes (Informatique) Örüntü tanıma sistemleri Pattern recognition systems Mustererkennung MATLAB
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014830687&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Item Description:	Includes bibliographical references and index
Physical Description:	XVI, 837 S. Ill., graph. Darst.
ISBN:	0123695317 9780123695314

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adam_text	PATTERN RECOGNITION THIRD EDITION SERGIOS THEODORIDIS DEPARTMENT OF INFORMATICS AND TELECOMMUNICATIONS UNIVERSITY OF ATHENS GREECE AND KONSTANTINOS KOUTROUMBAS INSTITUTE FOR SPACE APPLICATIONS & REMOTE SENSING NATIONAL OBSERVATORY OF ATHENS GREECE AMSTERDAM * BOSTON * HEIDELBERG * LONDON F^FFII NEW YORK * OXFORD * PARIS * SAN DIEGO JFCAELALAB. SAN FRANCISCO SINGAPORE * SYDNEY * TOKYO ELSEVIER ACADEMIC PRESS IS AN IMPRINT OF ELSEVIER CONTENTS PREFACE CHAPTER 1 INTRODUCTION 1.1 IS PATTERN RECOGNITION IMPORTANT? 1.2 FEATURES, FEATURE VECTORS, AND CLASSIFIERS 1.3 SUPERVISED VERSUS UNSUPERVISED PATTERN RECOGNITION 1.4 OUTLINE OF THE BOOK XV 1 1 3 CHAPTER 2 CLASSIFIERS BASED ON BAYES DECISION THEORY 13 2.1 2.2 2.3 2.4 2.5 2.6 2.7 INTRODUCTION BAYES DECISION THEORY DISCRIMINANT FUNCTIONS AND DECISION SURFACES BAYESIAN CLASSIFICATION FOR NORMAL DISTRIBUTIONS ESTIMATION OF UNKNOWN PROBABILITY DENSITY FUNCTIONS 2.5.1 2.5.2 2.5.3 2.5.4 2.5.5 2.5.6 2.5.7 MAXIMUM LIKELIHOOD PARAMETER ESTIMATION MAXIMUM A POSTERIORI PROBABILITY ESTIMATION BAYESIAN INFERENCE MAXIMUM ENTROPY ESTIMATION MIXTURE MODELS NONPARAMETRIC ESTIMATION THE NAIVE-BAYES CLASSIFIER THE NEAREST NEIGHBOR RULE BAYESIAN NETWORKS CHAPTER 3 LINEAR CLASSIFIERS 3.1 3.2 INTRODUCTION LINEAR DISCRIMINANT FUNCTIONS AND DECISION 13 13 19 20 28 28 32 33 35 36 42 47 48 50 69 69 HYPERPLANES 69 VI CONTENTS 3.3 THE PERCEPTRON ALGORITHM 71 3.4 LEAST SQUARES METHODS 79 3.4.1 MEAN SQUARE ERROR ESTIMATION 79 3.4.2 STOCHASTIC APPROXIMATION AND THE LMS ALGORITHM 82 3.4.3 SUM OF ERROR SQUARES ESTIMATION 84 3.5 MEAN SQUARE ESTIMATION REVISITED 86 3.5.1 MEAN SQUARE ERROR REGRESSION 86 3.5.2 MSE ESTIMATES POSTERIOR CLASS PROBABILITIES 87 3.5.3 THE BIAS-VARIANCE DILEMMA 90 3.6 LOGISTIC DISCRIMINATION 91 3.7 SUPPORT VECTOR MACHINES 93 3.7.1 SEPARABLE CLASSES 93 3.7.2 NONSEPARABLE CLASSES 98 3.7.3 V-SVM 106 3.7.4 SUPPORT VECTOR MACHINES: A GEOMETRIE VIEWPOINT 110 3.7.5 REDUCED CONVEX HULLS 112 CHAFTER 4 NONLINEAR CLASS1FIERS 121 4.1 INTRODUCTION 121 4.2 THE XOR PROBLEM 121 4.3 THE TWO-LAYER PERCEPTRON 122 4.3.1 CLASSIFICATION CAPABILITIES OF THE TWO-LAYER PERCEPTRON 126 4.4 THREE-LAYER PERCEPTRONS 129 4.5 ALGORITHMS BASED ON EXACT CLASSIFICATION OF THE TRAINING SET 130 4.6 THE BACKPROPAGATION ALGORITHM 132 4.7 VARIATIONS ON THE BACKPROPAGATION THEME 140 4.8 THE COST FUNCTION CHOICE 143 4.9 CHOICE OF THE NETWORK SIZE 147 4.10 A SIMULATION EXAMPLE 153 4.11 NETWORKS WITH WEIGHT SHARING 155 4.12 GENERALIZED LINEAR CLASSIFIERS 156 4.13 CAPACITY OF THE Z-DIMENSIONAL SPACE IN LINEAR DICHOTOMIES 4.14 POLYNOMIAL CLASSIFIERS 4.15 RADIAL BASIS FUNCTION NETWORKS 4.16 UNIVERSAL APPROXIMATORS 4.17 SUPPORT VECTOR MACHINES: THE NONLINEAR CASE CONTENTS VII 4.18 DECISION TREES 174 4.18.1 SET OF QUESTIONS 176 4.18.2 SPLITTING CRITERION 177 4.18.3 STOP-SPLITTING RULE 178 4.18.4 CLASS ASSIGNMENT RULE 178 4.19 COMBINING CLASSIFIERS 181 4.19.1 GEOMETRIE AVERAGE RULE 182 4.19.2 ARITHMETIC AVERAGE RULE 182 4.19.3 MAJORITY VOTING RULE 183 4.19.4 A BAYESIAN VIEWPOINT 185 4.20 THE BOOSTING APPROACH TO COMBINE CLASSIFIERS 188 4.21 DISCUSSION 196 CHAPTER 5 FEATURE SELECTION 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 213 INTRODUCTION 213 PREPROCESSING 214 5.2.1 OUTLIER REMOVAL 214 5.2.2 DATA NORMALIZATION 215 5.2.3 MISSING DATA 215 FEATURE SELECTION BASED ON STATISTICAL HYPOTHESIS TESTING 216 5.3.1 HYPOTHESIS TESTING BASICS 216 5.3.2 APPLICATION OF THE I-TEST IN FEATURE SELECTION 221 THE RECEIVER OPERATING CHARACTERISTICS (ROC) CURVE 223 CLASS SEPARABILITY MEASURES 224 5.5.1 DIVERGENCE 225 5.5.2 CHERNOFF BOUND AND BHATTACHARYYA DISTANCE 227 5.5.3 SCATTER MATRICES 228 FEATURE SUBSET SELECTION 231 5.6.1 SCALAR FEATURE SELECTION 232 5.6.2 FEATURE VECTOR SELECTION 233 OPTIMAL FEATURE GENERATION 237 NEURAL NETWORKS AND FEATURE GENERATION/SELECTION 242 A HINT ON GENERALIZATION THEORY 243 THE BAYESIAN INFORMATION CRITERION 253 CHAPTER 6 FEATURE GENERATION I: LFNEAR TRANSFORMS 263 6.1 INTRODUCTION 263 6.2 BASIS VECTORS AND IMAGES 264 VLLL CONTENTS 6.3 THE KARHUNEN-LOEVE TRANSFORM 266 6.4 THE SINGULAR VALUE DECOMPOSITION 273 6.5 INDEPENDENT COMPONENT ANALYSIS 276 6.5.1 ICA BASED ON SECOND- AND FOURTH-ORDER CUMULANTS 278 6.5.2 ICA BASED ON MUTUAL INFORMATION 280 6.5.3 AN ICA SIMULATION EXAMPLE 283 6.6 THE DISCRETE FOURIER TRANSFORM (DFT) 285 6.6.1 ONE-DIMENSIONAL DFT 285 6.6.2 TWO-DIMENSIONAL DFT 287 6.7 THE DISCRETE COSINE AND SINE TRANSFORMS 288 6.8 THE HADAMARD TRANSFORM 290 6.9 THE HAAR TRANSFORM 291 6.10 THE HAAR EXPANSION REVISITED 292 6.11 DISCRETE TIME WAVELET TRANSFORM (DTWT) 297 6.12 THE MULTIRESOLUTION INTERPRETATION 307 6.13 WAVELET PACKETS 309 6.14 A LOOK AT TWO-DIMENSIONAL GENERALIZATIONS 311 6.15 APPLICATIONS 313 CHAPTER 7 FEATURE GENERATION II 327 7.1 INTRODUCTION 327 7.2 REGIONAL FEATURES 328 7.2.1 FEATURES FOR TEXTURE CHARACTERIZATION 328 7.2.2 LOCAL LINEAR TRANSFORMS FOR TEXTURE FEATURE EXTRACTION 7.2.3 MOMENTS 7.2.4 PARAMETRIC MODELS 7.3 FEATURES FOR SHAPE AND SIZE CHARACTERIZATION 7.3.1 FOURIER FEATURES 7.3.2 CHAIN CODES 7.3.3 MOMENT-BASED FEATURES 7.3.4 GEOMETRIE FEATURES 7.4 A GLIMPSE AT FRACTALS 7.4.1 SELF-SIMILARITY AND FRACTAL DIMENSION 7.4.2 FRACTIONAL BROWNIARR MOTION 7.5 TYPICAL FEATURES FOR SPEECH AND AUDIO CLASSIFICATION 7.5.1 SHORT TIME PROCESSING OF SIGNALS 7.5.2 CEPSTRUM 7.5.3 THE MEL-CEPSTRUM 7.5.4 SPECIAL FEATURES 7.5.5 7.5.6 CONTENTS TIME DOMAIN FEATURES AN EXAMPLE CHARTER 8 TEMPLATE MATCHING 8.1 8.2 8.3 8.4 INTRODUCTION MEASURES BASED ON OPTIMAL PATH SEARCHING TECHNIQUES 8.2.1 8.2.2 8.2.3 BELLMAN S OPTIMALITY PRINCIPLE AND DYNAMIC PROGRAMMING THE EDIT DISTANCE DYNAMIC TIME WARPING IN SPEECH RECOGNITION MEASURES BASED ON CORRELATIONS DEFORMABLE TEMPLATE MODELS IX 383 384 397 397 398 400 401 406 413 419 CHARTER 9 CONTEXT-DEPENDENT CLASSIFICATION 9.1 INTRODUCTION 9.2 THE BAYES CLASSIFIER 9.3 MARKOV CHAIN MODELS 9.4 THE VITERBI ALGORITHM 9.5 CHANNEL EQUALIZATION 9.6 HIDDEN MARKOV MODELS 9.7 HMM WITH STATE DURATION MODELING 9.8 TRAINING MARKOV MODELS VIA NEURAL NETWORKS 9.9 A DISCUSSION OF MARKOV RANDOM FIELDS 427 427 427 428 429 432 437 452 458 460 CHARTER 10 SYSTEM EVALUATION 10.1 INTRODUCTION 10.2 ERROR COUNTING APPROACH 10.3 EXPLOITING THE FINITE SIZE OF THE DATA SET 10.4 A CASE STUDY FROM MEDICAL IMAGING CHAPTER 11 CLUSTERING: BASIC CONCEPTS 11.1 INTRODUCTION 11.1.1 APPLICATIONS OF CLUSTER ANALYSIS 11.1.2 TYPES OF FEATURES 11.1.3 DEFINITIONS OF CLUSTERING 11.2 PROXIMITY MEASURES 11.2.1 DEFINITIONS 11.2.2 PROXIMITY MEASURES BETWEEN TWO POINTS 471 471 471 473 476 483 483 486 487 488 490 490 493 X CONTENTS 11.2.3 PROXIMITY FUNCTIONS BETWEEN A POINT AND A SET 505 11.2.4 PROXIMITY FUNCTIONS BETWEEN TWO SETS 510 CHAPTER 12 CLUSTERINGALGORITHMSI: SEQUENTIAL ALGORITHMS 517 12.1 INTRODUCTION 517 12.1.1 NUMBER OF POSSIBLE CLUSTERINGS 517 12.2 CATEGORIES OF CLUSTERING ALGORITHMS 519 12.3 SEQUENTIAL CLUSTERING ALGORITHMS 523 12.3.1 ESTIMATION OF THE NUMBER OF CLUSTERS 525 12.4 AMODIFICATIONOFBSAS 527 12.5 ATWO-THRESHOLD SEQUENTIAL SCHEME 529 12.6 REFINEMENT STAGES 531 12.7 NEURAL NETWORK IMPLEMENTATION 533 12.7.1 DESCRIPTION OF THE ARCHITECTURE 533 12.7.2 IMPLEMENTATION OF THE BSAS ALGORITHM 535 CHAPTER 13 CLUSTERING ALGORITHMS II: HIERARCHICAL ALGORITHMS 541 13.1 13.2 13.3 13.4 13.5 13.6 INTRODUCTION AGGLOMERATIVE ALGORITHMS 13.2.1 13.2.2 13.2.3 13.2.4 13.2.5 13.2.6 DEFINITION OF SOME USEFUL QUANTITIES AGGLOMERATIVE ALGORITHMS BASED ON MATRIX THEORY MONOTONICITY AND CROSSOVER IMPLEMENTATIONAL ISSUES AGGLOMERATIVE ALGORITHMS BASED ON GRAPH THEORY TIES IN THE PROXIMITY MATRIX THE COPHENETIC MATRIX DIVISIVE ALGORITHMS HIERARCHICAL ALGORITHMS FOR LARGE DATA SETS CHOICE OF THE BEST NUMBER OF CLUSTERS 541 542 543 545 553 556 556 565 568 570 572 580 CHAPTER 14 CLUSTERING ALGORITHMS III: SCHEMES BASED ON FUNCTION OPTIMIZATION 5 89 14.1 INTRODUCTION 589 14.2 MIXTURE DECOMPOSITION SCHEMES 591 14.2.1 COMPACT AND HYPERELLIPSOIDAL CLUSTERS 593 14.2.2 A GEOMETRICAL INTERPRETATION 597 CONTENTS XI 14.3 FUZZY CLUSTERING ALGORITHMS 600 14.3.1 POINT REPRESENTATIVES 605 14.3.2 QUADRIC SURFACES AS REPRESENTATIVES 607 14.3.3 HYPERPLANE REPRESENTATIVES 617 14.3.4 COMBINING QUADRIC AND HYPERPLANE REPRESENTATIVES 619 14.3.5 A GEOMETRICAL INTERPRETATION 621 14.3.6 CONVERGENCE ASPECTS OF THE FUZZY CLUSTERING ALGORITHMS 622 14.3.7 ALTERNATING CLUSTER ESTIMATION 622 14.4 POSSIBILISTIC CLUSTERING 622 14.4.1 THE MODE-SEEKING PROPERTY 626 14.4.2 AN ALTERNATIVE POSSIBILISTIC SCHEME 629 14.5 HARD CLUSTERING ALGORITHMS 629 14.5.1 THE ISODATA OR K-MEANS OR C-MEANS ALGORITHM 631 14.5.2 FC-MEDOIDS ALGORITHMS 635 14.6 VECTOR QUANTIZATION 639 CHAPTER 15 CLUSTERING ALGORITHMS IV 653 15.1 INTRODUCTION 653 15.2 CLUSTERING ALGORITHMS BASED ON GRAPH THEORY 653 15.2.1 MINIMUM SPANNING TREE ALGORITHMS 654 15.2.2 ALGORITHMS BASED ON REGIONS OF INFLUENCE 657 15.2.3 ALGORITHMS BASED ON DIRECTED TREES 658 15.3 COMPETITIVE LEARNING ALGORITHMS 660 15.3.1 B ASIC COMPETITIVE LEARNING ALGORITHM 662 15.3.2 LEAKY LEARNING ALGORITHM 664 15.3.3 CONSCIENTIOUS COMPETITIVE LEARNING ALGORITHMS 665 15.3.4 COMPETITIVE LEARNING-LIKE ALGORITHMS ASSOCIATED WITH COST FUNCTIONS 666 15.3.5 SELF-ORGANIZING MAPS 667 15.3.6 SUPERVISED LEARNING VECTOR QUANTIZATION 668 15.4 BINARY MORPHOLOGY CLUSTERING ALGORITHMS (BMCAS) 669 15.4.1 DISCRETIZATION 669 15.4.2 MORPHOLOGICAL OPERATIONS 670 15.4.3 DETERMINATION OF THE CLUSTERS IN A DISCRETE BINARY SET 673 CONTENTS 15.4.4 ASSIGNMENT OF FEATURE VECTORS TO CLUSTERS 675 15.4.5 THE ALGORITHMIC SCHEINE 675 15.5 BOUNDARY DETECTION ALGORITHMS 678 15.6 VALLEY-SEEKING CLUSTERING ALGORITHMS 681 15.7 CLUSTERING VIA COST OPTIMIZATION (REVISITED) 683 15.7.1 BRANCH AND BOUND CLUSTERING ALGORITHMS 683 15.7.2 SIMULATED ANNEALING 687 15.7.3 DETERMINISTIC ANNEALING 688 15.7.4 CLUSTERING USING GENETIC ALGORITHMS 690 15.8 KERNEL CLUSTERING METHODS 692 15.9 DENSITY-BASED ALGORITHMS FOR LARGE DATA SETS 695 15.9.1 THE DBSCAN ALGORITHM 696 15.9.2 THE DBCLASD ALGORITHM 699 15.9.3 THE DENCLUE ALGORITHM 700 15.10 CLUSTERING ALGORITHMS FOR HIGH-DIMENSIONAL DATA SETS 702 15.10.1 DIMENSIONALITY REDUCTION CLUSTERING APPROACH 703 15.10.2 SUBSPACE CLUSTERING APPROACH 705 15.11 OTHER CLUSTERING ALGORITHMS 718 CHAPTER 16 CLUSTER VALIDITY 16.1 16.2 16.3 16.4 16.5 16.6 APPENDIX A HINTS FROM PROBABILITY AND STATISTICS 733 733 734 736 738 744 747 750 756 763 763 764 766 770 785 INTRODUCTION HYPOTHESIS TESTING REVISITED HYPOTHESIS TESTING IN CLUSTER VALIDITY 16.3.1 EXTERNAL CRITERIA 16.3.2 INTERNAL CRITERIA RELATIVE CRITERIA 16.4.1 HARD CLUSTERING 16.4.2 FUZZY CLUSTERING VALIDITY OF INDIVIDUAL CLUSTERS 16.5.1 EXTERNAL CRITERIA 16.5.2 INTERNAL CRITERIA CLUSTERING TENDENCY 16.6.1 TESTS FOR SPATIAL RANDOMNESS APPENDIX B LINEAR ALGEBRA BASICS 797 CONTENTS APPENDIX C COST FUNCTION OPTIMIZATION 801 APPENDIX D BASIC DEFINITIONS FROM LINEAR SYSTEMS THEORY 819 INDEX 823
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spellingShingle	Theodoridis, Sergios 1951- Pattern recognition Reconnaissance des formes (Informatique) Örüntü tanıma sistemleri Pattern recognition systems Mustererkennung (DE-588)4040936-3 gnd MATLAB (DE-588)4329066-8 gnd
subject_GND	(DE-588)4040936-3 (DE-588)4329066-8
title	Pattern recognition
title_auth	Pattern recognition
title_exact_search	Pattern recognition
title_full	Pattern recognition Sergios Theodoridis ; Konstantinos Koutroumbas
title_fullStr	Pattern recognition Sergios Theodoridis ; Konstantinos Koutroumbas
title_full_unstemmed	Pattern recognition Sergios Theodoridis ; Konstantinos Koutroumbas
title_short	Pattern recognition
title_sort	pattern recognition
topic	Reconnaissance des formes (Informatique) Örüntü tanıma sistemleri Pattern recognition systems Mustererkennung (DE-588)4040936-3 gnd MATLAB (DE-588)4329066-8 gnd
topic_facet	Reconnaissance des formes (Informatique) Örüntü tanıma sistemleri Pattern recognition systems Mustererkennung MATLAB
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014830687&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT theodoridissergios patternrecognition AT koutroumbaskonstantinos patternrecognition

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Branch Library Mathematics & Informatics

Holdings details from Teilbibliothek Mathematik & Informatik
Call Number:	0102 DAT 770f 2006 A 8662(3) Floor plan
Copy 1	Available for loan On Shelf