Pattern recognition:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam [u.a.]
Academic Press
2006
|
| Ausgabe: | 3. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XVI, 837 S. Ill., graph. Darst. |
| ISBN: | 0123695317 9780123695314 |
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| 100 | 1 | |a Theodoridis, Sergios |d 1951- |e Verfasser |0 (DE-588)12164135X |4 aut | |
| 245 | 1 | 0 | |a Pattern recognition |c Sergios Theodoridis ; Konstantinos Koutroumbas |
| 250 | |a 3. ed. | ||
| 264 | 1 | |a Amsterdam [u.a.] |b Academic Press |c 2006 | |
| 300 | |a XVI, 837 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Reconnaissance des formes (Informatique) | |
| 650 | 4 | |a Örüntü tanıma sistemleri | |
| 650 | 4 | |a Pattern recognition systems | |
| 650 | 0 | 7 | |a MATLAB |0 (DE-588)4329066-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Mustererkennung |0 (DE-588)4040936-3 |2 gnd |9 rswk-swf |
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| 689 | 0 | 1 | |a MATLAB |0 (DE-588)4329066-8 |D s |
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| 700 | 1 | |a Koutroumbas, Konstantinos |d 1967- |e Sonstige |0 (DE-588)136997937 |4 oth | |
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Datensatz im Suchindex
| _version_ | 1809768517875007488 |
|---|---|
| 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 |
| adam_txt |
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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| author | Theodoridis, Sergios 1951- |
| author_GND | (DE-588)12164135X (DE-588)136997937 |
| author_facet | Theodoridis, Sergios 1951- |
| author_role | aut |
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| author_variant | s t st |
| building | Verbundindex |
| bvnumber | BV021615545 |
| callnumber-first | T - Technology |
| callnumber-label | TK7882 |
| callnumber-raw | TK7882.P3 |
| callnumber-search | TK7882.P3 |
| callnumber-sort | TK 47882 P3 |
| callnumber-subject | TK - Electrical and Nuclear Engineering |
| classification_rvk | ST 330 ZN 6050 |
| classification_tum | DAT 770f |
| ctrlnum | (OCoLC)255219968 (DE-599)BVBBV021615545 |
| dewey-full | 006.4 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.4 |
| dewey-search | 006.4 |
| dewey-sort | 16.4 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Elektrotechnik / Elektronik / Nachrichtentechnik |
| discipline_str_mv | Informatik Elektrotechnik / Elektronik / Nachrichtentechnik |
| edition | 3. ed. |
| format | Book |
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| id | DE-604.BV021615545 |
| illustrated | Illustrated |
| index_date | 2024-07-02T14:52:04Z |
| indexdate | 2024-09-10T00:55:53Z |
| institution | BVB |
| isbn | 0123695317 9780123695314 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014830687 |
| oclc_num | 255219968 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM DE-M347 DE-83 DE-188 |
| owner_facet | DE-91G DE-BY-TUM DE-M347 DE-83 DE-188 |
| physical | XVI, 837 S. Ill., graph. Darst. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Academic Press |
| record_format | marc |
| spelling | Theodoridis, Sergios 1951- Verfasser (DE-588)12164135X aut Pattern recognition Sergios Theodoridis ; Konstantinos Koutroumbas 3. ed. Amsterdam [u.a.] Academic Press 2006 XVI, 837 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Reconnaissance des formes (Informatique) Örüntü tanıma sistemleri Pattern recognition systems MATLAB (DE-588)4329066-8 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf Mustererkennung (DE-588)4040936-3 s MATLAB (DE-588)4329066-8 s 1\p DE-604 Koutroumbas, Konstantinos 1967- Sonstige (DE-588)136997937 oth GBV Datenaustausch application/pdf 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 Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Theodoridis, Sergios 1951- Pattern recognition Reconnaissance des formes (Informatique) Örüntü tanıma sistemleri Pattern recognition systems MATLAB (DE-588)4329066-8 gnd Mustererkennung (DE-588)4040936-3 gnd |
| subject_GND | (DE-588)4329066-8 (DE-588)4040936-3 |
| title | Pattern recognition |
| title_auth | Pattern recognition |
| title_exact_search | Pattern recognition |
| title_exact_search_txtP | 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 MATLAB (DE-588)4329066-8 gnd Mustererkennung (DE-588)4040936-3 gnd |
| topic_facet | Reconnaissance des formes (Informatique) Örüntü tanıma sistemleri Pattern recognition systems MATLAB Mustererkennung |
| 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 |
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