Pattern recognition
A classic that offers comprehensive coverage with a balance between theory and practice.
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| Médium: | E-kniha Kniha |
| Jazyk: | angličtina |
| Vydáno: |
San Diego, CA ; London ; Tokyo
Academic Press, an imprint of Elsevier
2006
Elsevier Science & Technology |
| Vydání: | 3 |
| Témata: | |
| ISBN: | 0123695317, 9780123695314 |
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| Abstract | A classic that offers comprehensive coverage with a balance between theory and practice. |
|---|---|
| AbstractList | A classic that offers comprehensive coverage with a balance between theory and practice. |
| Author | Theodoridis, Sergios Koutroumbas, Konstantinos |
| Author_xml | – sequence: 1 fullname: Theodoridis, Sergios – sequence: 2 fullname: Koutroumbas, Konstantinos |
| BackLink | https://cir.nii.ac.jp/crid/1130000798275928576$$DView record in CiNii |
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| DOI | 10.1016/B978-0-12-369531-4.X5000-8 |
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| Discipline | Engineering Computer Science |
| EISBN | 9780080513614 0080513611 |
| Edition | 3 |
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| LCCN | 2002117797 |
| LCCallNum_Ident | TK7882.P3T46 2006 |
| Language | English |
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| Notes | Includes bibliographical references and index |
| OCLC | 239612960 |
| PQID | EBC344670 |
| PageCount | 854 |
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| PublicationCentury | 2000 |
| PublicationDate | c2006 2006 |
| PublicationDateYYYYMMDD | 2006-01-01 |
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| PublicationDecade | 2000 |
| PublicationPlace | San Diego, CA ; London ; Tokyo |
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| PublicationYear | 2006 |
| Publisher | Academic Press, an imprint of Elsevier Elsevier Science & Technology |
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| Snippet | A classic that offers comprehensive coverage with a balance between theory and practice. |
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| SourceType | Publisher |
| SubjectTerms | Pattern recognition systems |
| TableOfContents | A.10 THE CRAMER-RAO LOWER BOUND -- A.11 CENTRAL LIMIT THEOREM -- A.12 CHI-SQUARE DISTRIBUTION -- A.13 t-DISTRIBUTION -- A.14 BETA DISTRIBUTION -- A.15 POISSON DISTRIBUTION -- Appendix B LINEAR ALGEBRA BASICS -- B.1 POSITIVE DEFINITE AND SYMMETRIC MATRICES -- B.2 CORRELATION MATRIX DIAGONALIZATION -- Appendix C COST FUNCTION OPTIMIZATION -- C.1 GRADIENT DESCENT ALGORITHM -- C.2 NEWTON'S ALGORITHM -- C.3 CONJUGATE-GRADIENT METHOD -- C.4 OPTIMIZATION FOR CONSTRAINED PROBLEMS -- Appendix D BASIC DEFINITIONS FROM LINEAR SYSTEMS THEORY -- D.1 LINEAR TIME INVARIANT (LTI) SYSTEMS -- D.2 TRANSFER FUNCTION -- D.3 SERIAL AND PARALLEL CONNECTION -- D.4 TWO-DIMENSIONAL GENERALIZATIONS -- INDEX 5.5 CLASS SEPARABILITY MEASURES -- 5.6 FEATURE SUBSET SELECTION -- 5.7 OPTIMAL FEATURE GENERATION -- 5.8 NEURAL NETWORKS AND FEATURE GENERATION/ SELECTION -- 5.9 A HINT ON GENERALIZATION THEORY -- 5.10 THE BAYESIAN INFORMATION CRITERION -- 6 FEATURE GENERATION I: LINEAR TRANSFORMS -- 6.1 INTRODUCTION -- 6.2 BASIS VECTORS AND IMAGES -- 6.3 THE KARHUNEN-LOÈVE TRANSFORM -- 6.4 THE SINGULAR VALUE DECOMPOSITION -- 6.5 INDEPENDENT COMPONENT ANALYSIS -- 6.6 THE DISCRETE FOURIER TRANSFORM (DFT) -- 6.7 THE DISCRETE COSINE AND SINE TRANSFORMS -- 6.8 THE HADAMARD TRANSFORM -- 6.9 THE HAAR TRANSFORM -- 6.10 THE HAAR EXPANSION REVISITED -- 6.11 DISCRETE TIMEWAVELET TRANSFORM (DTWT) -- 6.12 THE MULTIRESOLUTION INTERPRETATION -- 6.13 WAVELET PACKETS -- 6.14 A LOOK AT TWO-DIMENSIONAL GENERALIZATIONS -- 6.15 APPLICATIONS -- 7 FEATURE GENERATION II -- 7.1 INTRODUCTION -- 7.2 REGIONAL FEATURES -- 7.3 FEATURES FOR SHAPE AND SIZE CHARACTERIZATION -- 7.4 A GLIMPSE AT FRACTALS -- 7.5 TYPICAL FEATURES FOR SPEECH AND AUDIO CLASSIFICATION -- 8 TEMPLATE MATCHING -- 8.1 INTRODUCTION -- 8.2 MEASURES BASED ON OPTIMAL PATH SEARCHING TECHNIQUES -- 8.3 MEASURES BASED ON CORRELATIONS -- 8.4 DEFORMABLE TEMPLATE MODELS -- 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 -- 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 -- 11 CLUSTERING: BASIC CONCEPTS -- 11.1 INTRODUCTION -- 11.2 PROXIMITY MEASURES -- 12 CLUSTERING ALGORITHMS I: SEQUENTIAL ALGORITHMS -- 12.1 INTRODUCTION 12.2 CATEGORIES OF CLUSTERING ALGORITHMS -- 12.3 SEQUENTIAL CLUSTERING ALGORITHMS -- 12.4 A MODIFICATION OF BSAS -- 12.5 A TWO-THRESHOLD SEQUENTIAL SCHEME -- 12.6 REFINEMENT STAGES -- 12.7 NEURAL NETWORK IMPLEMENTATION -- 13 CLUSTERING ALGORITHMS II: HIERARCHICAL ALGORITHMS -- 13.1 INTRODUCTION -- 13.2 AGGLOMERATIVE ALGORITHMS -- 13.3 THE COPHENETIC MATRIX -- 13.4 DIVISIVE ALGORITHMS -- 13.5 HIERARCHICAL ALGORITHMS FOR LARGE DATA SETS -- 13.6 CHOICE OF THE BEST NUMBER OF CLUSTERS -- 14 CLUSTERING ALGORITHMS III: SCHEMES BASED ON FUNCTION OPTIMIZATION -- 14.1 INTRODUCTION -- 14.2 MIXTURE DECOMPOSITION SCHEMES -- 14.3 FUZZY CLUSTERING ALGORITHMS -- 14.4 POSSIBILISTIC CLUSTERING -- 14.5 HARD CLUSTERING ALGORITHMS -- 14.6 VECTOR QUANTIZATION -- APPENDIX -- 15 CLUSTERING ALGORITHMS IV -- 15.1 INTRODUCTION -- 15.2 CLUSTERING ALGORITHMS BASED ON GRAPH THEORY -- 15.3 COMPETITIVE LEARNING ALGORITHMS -- 15.4 BINARY MORPHOLOGY CLUSTERING ALGORITHMS (BMCAs) -- 15.5 BOUNDARY DETECTION ALGORITHMS -- 15.6 VALLEY-SEEKING CLUSTERING ALGORITHMS -- 15.7 CLUSTERING VIA COST OPTIMIZATION (REVISITED) -- 15.8 KERNEL CLUSTERING METHODS -- 15.9 DENSITY-BASED ALGORITHMS FOR LARGE DATA SETS -- 15.10 CLUSTERING ALGORITHMS FOR HIGH-DIMENSIONAL DATA SETS -- 15.11 OTHER CLUSTERING ALGORITHMS -- 16 CLUSTER VALIDITY -- 16.1 INTRODUCTION -- 16.2 HYPOTHESIS TESTING REVISITED -- 16.3 HYPOTHESIS TESTING IN CLUSTER VALIDITY -- 16.4 RELATIVE CRITERIA -- 16.5 VALIDITY OF INDIVIDUAL CLUSTERS -- 16.6 CLUSTERING TENDENCY -- Appendix A HINTS FROM PROBABILITY AND STATISTICS -- A.1 TOTAL PROBABILITY AND THE BAYES RULE -- A.2 MEAN AND VARIANCE -- A.3 STATISTICAL INDEPENDENCE -- A.4 MARGINALIZATION -- A.5 CHARACTERISTIC FUNCTIONS -- A.6 MOMENTS AND CUMULANTS -- A.7 EDGEWORTH EXPANSION OF A PDF -- A.8 KULLBACK-LEIBLER DISTANCE -- A.9 MULTIVARIATE GAUSSIAN OR NORMAL PROBABILITY DENSITY FUNCTION Front cover -- Title page -- Copyright page -- Table of contents -- PREFACE -- 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 -- 2 CLASSIFIERS BASED ON BAYES DECISION THEORY -- 2.1 INTRODUCTION -- 2.2 BAYES DECISION THEORY -- 2.3 DISCRIMINANT FUNCTIONS AND DECISION SURFACES -- 2.4 BAYESIAN CLASSIFICATION FOR NORMAL DISTRIBUTIONS -- 2.5 ESTIMATION OF UNKNOWN PROBABILITY DENSITY FUNCTIONS -- 2.6 THE NEAREST NEIGHBOR RULE -- 2.7 BAYESIAN NETWORKS -- 3 LINEAR CLASSIFIERS -- 3.1 INTRODUCTION -- 3.2 LINEAR DISCRIMINANT FUNCTIONS AND DECISION HYPERPLANES -- 3.3 THE PERCEPTRON ALGORITHM -- 3.4 LEAST SQUARES METHODS -- 3.5 MEAN SQUARE ESTIMATION REVISITED -- 3.6 LOGISTIC DISCRIMINATION -- 3.7 SUPPORT VECTOR MACHINES -- 4 NONLINEAR CLASSIFIERS -- 4.1 INTRODUCTION -- 4.2 THE XOR PROBLEM -- 4.3 THE TWO-LAYER PERCEPTRON -- 4.4 THREE-LAYER PERCEPTRONS -- 4.5 ALGORITHMS BASED ON EXACT CLASSIFICATION OF THE TRAINING SET -- 4.6 THE BACKPROPAGATION ALGORITHM -- 4.7 VARIATIONS ON THE BACKPROPAGATION THEME -- 4.8 THE COST FUNCTION CHOICE -- 4.9 CHOICE OF THE NETWORK SIZE -- 4.10 A SIMULATION EXAMPLE -- 4.11 NETWORKS WITH WEIGHT SHARING -- 4.12 GENERALIZED LINEAR CLASSIFIERS -- 4.13 CAPACITY OF THE l-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 -- 4.18 DECISION TREES -- 4.19 COMBINING CLASSIFIERS -- 4.20 THE BOOSTING APPROACH TO COMBINE CLASSIFIERS -- 4.21 DISCUSSION -- 5 FEATURE SELECTION -- 5.1 INTRODUCTION -- 5.2 PREPROCESSING -- 5.3 FEATURE SELECTION BASED ON STATISTICAL HYPOTHESIS TESTING -- 5.4 THE RECEIVER OPERATING CHARACTERISTICS (ROC) CURVE |
| Title | Pattern recognition |
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