Pattern Recognition (eBook)
856 Seiten
Elsevier Science (Verlag)
978-0-08-051361-4 (ISBN)
Pattern recognition is integral to a wide spectrum of scientific disciplines and technologies including image analysis, speech recognition, audio classification, communications, computer-aided diagnosis, and data mining. The authors, leading experts in the field of pattern recognition, have once again provided an up-to-date, self-contained volume encapsulating this wide spectrum of information.
Each chapter is designed to begin with basics of theory progressing to advanced topics and then discusses cutting-edge techniques. Problems and exercises are present at the end of each chapter with a solutions manual provided via a companion website where a number of demonstrations are also available to aid the reader in gaining practical experience with the theories and associated algorithms.
This edition includes discussion of Bayesian classification, Bayesian networks, linear and nonlinear classifier design (including neural networks and support vector machines), dynamic programming and hidden Markov models for sequential data, feature generation (including wavelets, principal component analysis, independent component analysis and fractals), feature selection techniques, basic concepts from learning theory, and clustering concepts and algorithms. This book considers classical and current theory and practice, of both supervised and unsupervised pattern recognition, to build a complete background for professionals and students of engineering.
FOR INSTRUCTORS: To obtain access to the solutions manual for this title simply register on our textbook website (textbooks.elsevier.com)and request access to the Computer Science or Electronics and Electrical Engineering subject area. Once approved (usually within one business day) you will be able to access all of the instructor-only materials through the Instructor Manual link on this book's full web page.
* The latest results on support vector machines including v-SVM's and their geometric interpretation
* Classifier combinations including the Boosting approach
* State-of-the-art material for clustering algorithms tailored for large data sets and/or high dimensional data, as required by applications such as web-mining and bioinformatics
* Coverage of diverse applications such as image analysis, optical character recognition, channel equalization, speech recognition and audio classification
Pattern recognition is a fast growing area with applications in a widely diverse number of fields such as communications engineering, bioinformatics, data mining, content-based database retrieval, to name but a few. This new edition addresses and keeps pace with the most recent advancements in these and related areas. This new edition: a) covers Data Mining, which was not treated in the previous edition, and is integrated with existing material in the book, b) includes new results on Learning Theory and Support Vector Machines, that are at the forefront of today's research, with a lot of interest both in academia and in applications-oriented communities, c) for the first time treats audio along with image applications since in today's world the most advanced applications are treated in a unified way and d) the subject of classifier combinations is treated, since this is a hot topic currently of interest in the pattern recognition community. - The latest results on support vector machines including v-SVM's and their geometric interpretation- Classifier combinations including the Boosting approach- State-of-the-art material for clustering algorithms tailored for large data sets and/or high dimensional data, as required by applications such as web-mining and bioinformatics- Coverage of diverse applications such as image analysis, optical character recognition, channel equalization, speech recognition and audio classification
Cover 1
Table of contents 6
PREFACE 16
1 INTRODUCTION 18
1.1 IS PATTERN RECOGNITION IMPORTANT? 18
1.2 FEATURES, FEATURE VECTORS, AND CLASSIFIERS 20
1.3 SUPERVISED VERSUS UNSUPERVISED PATTERN RECOGNITION 23
1.4 OUTLINE OF THE BOOK 25
2 CLASSIFIERS BASED ON BAYES DECISION THEORY 30
2.1 INTRODUCTION 30
2.2 BAYES DECISION THEORY 30
2.3 DISCRIMINANT FUNCTIONS AND DECISION SURFACES 36
2.4 BAYESIAN CLASSIFICATION FOR NORMAL DISTRIBUTIONS 37
2.5 ESTIMATION OF UNKNOWN PROBABILITY DENSITY FUNCTIONS 45
2.6 THE NEAREST NEIGHBOR RULE 65
2.7 BAYESIAN NETWORKS 67
3 LINEAR CLASSIFIERS 86
3.1 INTRODUCTION 86
3.2 LINEAR DISCRIMINANT FUNCTIONS AND DECISION HYPERPLANES 86
3.3 THE PERCEPTRON ALGORITHM 88
3.4 LEAST SQUARES METHODS 96
3.5 MEAN SQUARE ESTIMATION REVISITED 103
3.6 LOGISTIC DISCRIMINATION 108
3.7 SUPPORT VECTOR MACHINES 110
4 NONLINEAR CLASSIFIERS 138
4.1 INTRODUCTION 138
4.2 THE XOR PROBLEM 138
4.3 THE TWO-LAYER PERCEPTRON 139
4.4 THREE-LAYER PERCEPTRONS 146
4.5 ALGORITHMS BASED ON EXACT CLASSIFICATION OF THE TRAINING SET 147
4.6 THE BACKPROPAGATION ALGORITHM 149
4.7 VARIATIONS ON THE BACKPROPAGATION THEME 157
4.8 THE COST FUNCTION CHOICE 160
4.9 CHOICE OF THE NETWORK SIZE 164
4.10 A SIMULATION EXAMPLE 170
4.11 NETWORKS WITH WEIGHT SHARING 172
4.12 GENERALIZED LINEAR CLASSIFIERS 173
4.13 CAPACITY OF THE l-DIMENSIONAL SPACE IN LINEAR DICHOTOMIES 175
4.14 POLYNOMIAL CLASSIFIERS 178
4.15 RADIAL BASIS FUNCTION NETWORKS 179
4.16 UNIVERSAL APPROXIMATORS 184
4.17 SUPPORT VECTOR MACHINES: THE NONLINEAR CASE 186
4.18 DECISION TREES 191
4.19 COMBINING CLASSIFIERS 198
4.20 THE BOOSTING APPROACH TO COMBINE CLASSIFIERS 205
4.21 DISCUSSION 213
5 FEATURE SELECTION 230
5.1 INTRODUCTION 230
5.2 PREPROCESSING 231
5.3 FEATURE SELECTION BASED ON STATISTICAL HYPOTHESIS TESTING 233
5.4 THE RECEIVER OPERATING CHARACTERISTICS (ROC) CURVE 240
5.5 CLASS SEPARABILITY MEASURES 241
5.6 FEATURE SUBSET SELECTION 248
5.7 OPTIMAL FEATURE GENERATION 254
5.8 NEURAL NETWORKS AND FEATURE GENERATION/ SELECTION 259
5.9 A HINT ON GENERALIZATION THEORY 260
5.10 THE BAYESIAN INFORMATION CRITERION 270
6 FEATURE GENERATION I: LINEAR TRANSFORMS 280
6.1 INTRODUCTION 280
6.2 BASIS VECTORS AND IMAGES 281
6.3 THE KARHUNEN–LOÈVE TRANSFORM 283
6.4 THE SINGULAR VALUE DECOMPOSITION 290
6.5 INDEPENDENT COMPONENT ANALYSIS 293
6.6 THE DISCRETE FOURIER TRANSFORM (DFT) 302
6.7 THE DISCRETE COSINE AND SINE TRANSFORMS 305
6.8 THE HADAMARD TRANSFORM 307
6.9 THE HAAR TRANSFORM 308
6.10 THE HAAR EXPANSION REVISITED 309
6.11 DISCRETE TIMEWAVELET TRANSFORM (DTWT) 314
6.12 THE MULTIRESOLUTION INTERPRETATION 324
6.13 WAVELET PACKETS 326
6.14 A LOOK AT TWO-DIMENSIONAL GENERALIZATIONS 328
6.15 APPLICATIONS 330
7 FEATURE GENERATION II 344
7.1 INTRODUCTION 344
7.2 REGIONAL FEATURES 345
7.3 FEATURES FOR SHAPE AND SIZE CHARACTERIZATION 370
7.4 A GLIMPSE AT FRACTALS 379
7.5 TYPICAL FEATURES FOR SPEECH AND AUDIO CLASSIFICATION 387
8 TEMPLATE MATCHING 414
8.1 INTRODUCTION 414
8.2 MEASURES BASED ON OPTIMAL PATH SEARCHING TECHNIQUES 415
8.3 MEASURES BASED ON CORRELATIONS 430
8.4 DEFORMABLE TEMPLATE MODELS 436
9 CONTEXT-DEPENDENT CLASSIFICATION 444
9.1 INTRODUCTION 444
9.2 THE BAYES CLASSIFIER 444
9.3 MARKOV CHAIN MODELS 445
9.4 THE VITERBI ALGORITHM 446
9.5 CHANNEL EQUALIZATION 449
9.6 HIDDEN MARKOV MODELS 454
9.7 HMM WITH STATE DURATION MODELING 469
9.8 TRAINING MARKOV MODELS VIA NEURAL NETWORKS 475
9.9 A DISCUSSION OF MARKOV RANDOM FIELDS 477
10 SYSTEM EVALUATION 488
10.1 INTRODUCTION 488
10.2 ERROR COUNTING APPROACH 488
10.3 EXPLOITING THE FINITE SIZE OF THE DATA SET 490
10.4 A CASE STUDY FROM MEDICAL IMAGING 493
11 CLUSTERING: BASIC CONCEPTS 500
11.1 INTRODUCTION 500
11.2 PROXIMITY MEASURES 507
12 CLUSTERING ALGORITHMS I: SEQUENTIAL ALGORITHMS 534
12.1 INTRODUCTION 534
12.2 CATEGORIES OF CLUSTERING ALGORITHMS 536
12.3 SEQUENTIAL CLUSTERING ALGORITHMS 540
12.4 A MODIFICATION OF BSAS 544
12.5 A TWO-THRESHOLD SEQUENTIAL SCHEME 546
12.6 REFINEMENT STAGES 548
12.7 NEURAL NETWORK IMPLEMENTATION 550
13 CLUSTERING ALGORITHMS II: HIERARCHICAL ALGORITHMS 558
13.1 INTRODUCTION 558
13.2 AGGLOMERATIVE ALGORITHMS 559
13.3 THE COPHENETIC MATRIX 585
13.4 DIVISIVE ALGORITHMS 587
13.5 HIERARCHICAL ALGORITHMS FOR LARGE DATA SETS 589
13.6 CHOICE OF THE BEST NUMBER OF CLUSTERS 597
14 CLUSTERING ALGORITHMS III: SCHEMES BASED ON FUNCTION OPTIMIZATION 606
14.1 INTRODUCTION 606
14.2 MIXTURE DECOMPOSITION SCHEMES 608
14.3 FUZZY CLUSTERING ALGORITHMS 617
14.4 POSSIBILISTIC CLUSTERING 639
14.5 HARD CLUSTERING ALGORITHMS 646
14.6 VECTOR QUANTIZATION 656
APPENDIX 659
15 CLUSTERING ALGORITHMS IV 670
15.1 INTRODUCTION 670
15.2 CLUSTERING ALGORITHMS BASED ON GRAPH THEORY 670
15.3 COMPETITIVE LEARNING ALGORITHMS 677
15.4 BINARY MORPHOLOGY CLUSTERING ALGORITHMS (BMCAs) 686
15.5 BOUNDARY DETECTION ALGORITHMS 695
15.6 VALLEY-SEEKING CLUSTERING ALGORITHMS 698
15.7 CLUSTERING VIA COST OPTIMIZATION (REVISITED) 700
15.8 KERNEL CLUSTERING METHODS 709
15.9 DENSITY-BASED ALGORITHMS FOR LARGE DATA SETS 712
15.10 CLUSTERING ALGORITHMS FOR HIGH-DIMENSIONAL DATA SETS 719
15.11 OTHER CLUSTERING ALGORITHMS 735
16 CLUSTER VALIDITY 750
16.1 INTRODUCTION 750
16.2 HYPOTHESIS TESTING REVISITED 751
16.3 HYPOTHESIS TESTING IN CLUSTER VALIDITY 753
16.4 RELATIVE CRITERIA 764
16.5 VALIDITY OF INDIVIDUAL CLUSTERS 780
16.6 CLUSTERING TENDENCY 783
Appendix A HINTS FROM PROBABILITY AND STATISTICS 802
A.1 TOTAL PROBABILITY AND THE BAYES RULE 802
A.2 MEAN AND VARIANCE 803
A.3 STATISTICAL INDEPENDENCE 803
A.4 MARGINALIZATION 803
A.5 CHARACTERISTIC FUNCTIONS 804
A.6 MOMENTS AND CUMULANTS 804
A.7 EDGEWORTH EXPANSION OF A PDF 806
A.8 KULLBACK–LEIBLER DISTANCE 806
A.9 MULTIVARIATE GAUSSIAN OR NORMAL PROBABILITY DENSITY FUNCTION 807
A.10 THE CRAMER–RAO LOWER BOUND 809
A.11 CENTRAL LIMIT THEOREM 810
A.12 CHI-SQUARE DISTRIBUTION 810
A.13 t-DISTRIBUTION 812
A.14 BETA DISTRIBUTION 812
A.15 POISSON DISTRIBUTION 813
Appendix B LINEAR ALGEBRA BASICS 814
B.1 POSITIVE DEFINITE AND SYMMETRIC MATRICES 814
B.2 CORRELATION MATRIX DIAGONALIZATION 815
Appendix C COST FUNCTION OPTIMIZATION 818
C.1 GRADIENT DESCENT ALGORITHM 818
C.2 NEWTON’S ALGORITHM 822
C.3 CONJUGATE-GRADIENT METHOD 823
C.4 OPTIMIZATION FOR CONSTRAINED PROBLEMS 823
Appendix D BASIC DEFINITIONS FROM LINEAR SYSTEMS THEORY 836
D.1 LINEAR TIME INVARIANT (LTI) SYSTEMS 836
D.2 TRANSFER FUNCTION 837
D.3 SERIAL AND PARALLEL CONNECTION 838
D.4 TWO-DIMENSIONAL GENERALIZATIONS 839
INDEX 840
| Erscheint lt. Verlag | 7.4.2006 |
|---|---|
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Naturwissenschaften ► Physik / Astronomie ► Elektrodynamik | |
| Sozialwissenschaften ► Kommunikation / Medien ► Buchhandel / Bibliothekswesen | |
| Technik ► Elektrotechnik / Energietechnik | |
| Technik ► Nachrichtentechnik | |
| ISBN-10 | 0-08-051361-1 / 0080513611 |
| ISBN-13 | 978-0-08-051361-4 / 9780080513614 |
| Haben Sie eine Frage zum Produkt? |
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