Statistical pattern recognition:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Chichester
Wiley
2003
|
| Ausgabe: | 2. ed., repr. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XVIII, 496 S. graph. Darst. |
| ISBN: | 0470845139 0470845147 |
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Datensatz im Suchindex
| _version_ | 1804130587878883328 |
|---|---|
| adam_text | Titel: Statistical pattern recognition
Autor: Webb, Andrew R
Jahr: 2003
Contents
Preface xv
Notation xvii
1 Introduction to statistical pattern recognition 1
1.1 Statistical pattern recognition 1
1.1.1 Introduction 1
1.1.2 The basic model 2
1.2 Stages in a pattern recognition problem 3
1.3 Issues 4
1.4 Supervised versus unsupervised 5
1.5 Approaches to statistical pattern recognition 6
1.5.1 Elementary decision theory 6
1.5.2 Discriminant functions 19
1.6 Multiple regression 25
1.7 Outline of book 27
1.8 Notes and references 28
Exercises 30
2 Density estimation - parametric 33
2.1 Introduction 33
2.2 Normal-based models 34
2.2.1 Linear and quadratic discriminant functions 34
2.2.2 Regularised discriminant analysis 37
2.2.3 Example application study 38
2.2.4 Further developments 40
2.2.5 Summary 40
2.3 Normal mixture models 41
2.3.1 Maximum likelihood estimation via EM 41
2.3.2 Mixture models for discrimination 45
2.3.3 How many components? 46
2.3.4 Example application study 47
2.3.5 Further developments 49
2.3.6 Summary 49
viii CONTENTS
2.4 Bayesian estimates
2.4.1 Bayesian learning methods
2.4.2 Markov chain Monte Carlo
2.4.3 Bayesian approaches to discrimination
2.4.4 Example application study
2.4.5 Further developments
2.4.6 Summary
2.5 Application studies
2.6 Summary and discussion
2.7 Recommendations
2.8 Notes and references
Exercises
3 Density estimation - nonparametric
3.1 Introduction
3.2 Histogram method
3.2.1 Data-adaptive histograms
3.2.2 Independence assumption
3.2.3 Lancaster models
3.2.4 Maximum weight dependence trees
3.2.5 Bayesian networks
3.2.6 Example application study
3.2.7 Further developments
3.2.8 Summary
3.3 -nearest-neighbour method
3.3.1 k - nearest- nei ghbour decision rule
3.3.2 Properties of the nearest-neighbour rule
3.3.3 Algorithms
3.3.4 Editing techniques
3.3.5 Choice of distance metric
3.3.6 Example application study
3.3.7 Further developments
3.3.8 Summary
3.4 Expansion by basis functions
3.5 Kernel methods
3.5.1 Choice of smoothing parameter
3.5.2 Choice of kernel
3.5.3 Example application study
3.5.4 Further developments
3.5.5 Summary
3.6 Application studies
3.7 Summary and discussion
3.8 Recommendations
3.9 Notes and references
Exercises
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CONTENTS ix
4 Linear discriminant analysis 123
4.1 Introduction 123
4.2 Two-class algorithms 124
4.2.1 General ideas 124
4.2.2 Perceptron criterion 124
4.2.3 Fisher s criterion 128
4.2.4 Least mean squared error procedures 130
4.2.5 Support vector machines 134
4.2.6 Example application study 141
4.2.7 Further developments 142
4.2.8 Summary 142
4.3 Multiclass algorithms 144
4.3.1 General ideas 144
4.3.2 Error-correction procedure 145
4.3.3 Fisher s criterion - linear discriminant analysis 145
4.3.4 Least mean squared error procedures 148
4.3.5 Optimal scaling 152
4.3.6 Regularisation 155
4.3.7 Multiclass support vector machines 155
4.3.8 Example application study 156
4.3.9 Further developments 156
4.3.10 Summary 158
4.4 Logistic discrimination 158
4.4.1 Two-group case 158
4.4.2 Maximum likelihood estimation 159
4.4.3 Multiclass logistic discrimination 161
4.4.4 Example application study 162
4.4.5 Further developments 163
4.4.6 Summary 163
4.5 Application studies 163
4.6 Summary and discussion 164
4.7 Recommendations 165
4.8 Notes and references 165
Exercises 165
5 Nonlinear discriminant analysis - kernel methods 169
5.1 Introduction 169
5.2 Optimisation criteria 171
5.2.1 Least squares error measure 171
5.2.2 Maximum likelihood 175
5.2.3 Entropy 176
5.3 Radial basis functions 177
5.3.1 Introduction 177
5.3.2 Motivation 178
5.3.3 Specifying the model 181
x CONTENTS
187
187
189
189
190
191
5.3.4 Radial basis function properties
5.3.5 Simple radial basis function
5.3.6 Example application study [^7
5.3.7 Further developments
5.3.8 Summary
5.4 Nonlinear support vector machines
5.4.1 Types of kernel
5.4.2 Model selection *92
5.4.3 Support vector machines for regression 192
5.4.4 Example application study 195
5.4.5 Further developments 196
5.4.6 Summary 197
5.5 Application studies 197
5.6 Summary and discussion 199
5.7 Recommendations 199
5.8 Notes and references 200
Exercises 200
6 Nonlinear discriminant analysis - projection methods 203
6.1 Introduction 203
6.2 The multilayer perceptron 204
6.2.1 Introduction 204
6.2.2 Specifying the multilayer perceptron structure 205
6.2.3 Determining the multilayer perceptron weights 205
6.2.4 Properties 212
6.2.5 Example application study 213
6.2.6 Further developments 214
6.2.7 Summary 216
6.3 Projection pursuit 216
6.3.1 Introduction 216
6.3.2 Projection pursuit for discrimination 218
6.3.3 Example application study 219
6.3.4 Further developments 220
6.3.5 Summary 220
6.4 Application studies 221
6.5 Summary and discussion 221
6.6 Recommendations
6.7 Notes and references
Exercises
7 TVee-based methods
7.1 Introduction
222
223
223
225
7.2 Classification trees ^
225
7.2.1 Introduction
7.2.2 Classifier tn
7.2.3 Other issues
7.2.4 Example application study
7.2.2 Classifier tree construction
7.2.3 Other issues
239
CONTENTS xi
7.2.5 Further developments 239
7.2.6 Summary 240
7.3 Multivariate adaptive regression splines 241
7.3.1 Introduction 241
7.3.2 Recursive partitioning model 241
7.3.3 Example application study 244
7.3.4 Further developments 245
7.3.5 Summary 245
7.4 Application studies 245
7.5 Summary and discussion 247
7.6 Recommendations 247
7.7 Notes and references 248
Exercises 248
8 Performance 251
8.1 Introduction 251
8.2 Performance assessment 252
8.2.1 Discriminability 252
8.2.2 Reliability 258
8.2.3 ROC curves for two-class rules 260
8.2.4 Example application study 263
8.2.5 Further developments 264
8.2.6 Summary 265
8.3 Comparing classifier performance 266
8.3.1 Which technique is best? 266
8.3.2 Statistical tests 267
8.3.3 Comparing rules when misclassification costs are uncertain 267
8.3.4 Example application study 269
8.3.5 Further developments 270
8.3.6 Summary 271
8.4 Combining classifiers 271
8.4.1 Introduction 271
8.4.2 Motivation 272
8.4.3 Characteristics of a combination scheme 275
8.4.4 Data fusion 278
8.4.5 Classifier combination methods 284
8.4.6 Example application study 297
8.4.7 Further developments 298
8.4.8 Summary 298
8.5 Application studies 299
8.6 Summary and discussion 299
8.7 Recommendations ^00
8.8 Notes and references ^00
Exercises ^01
9 Feature selection and extraction
9.1 Introduction
305
305
xii CONTENTS
9.2
9.3
Feature selection
9.2.1 Feature selection criteria
Search algorithms for feature selection
Suboptimal search algorithms
Example application study
Further developments
Summary
Linear feature extraction
9.3.1 Principal components analysis
Karhunen-Loeve transformation
Factor analysis
Example application study
Further developments
Summary
9.2.2
9.2.3
9.2.4
9.2.5
9.2.6
9.3.2
9.3.3
9.3.4
9.3.5
9.3.6
9.4 Multidimensional scaling
9.4.1 Classical scaling
9.4.2 Metric multidimensional scaling
9.4.3 Ordinal scaling
9.4.4 Algorithms
9.4.5 Multidimensional scaling for feature extraction
9.4.6 Example application study
9.4.7 Further developments
9.4.8 Summary
9.5 Application studies
9.6 Summary and discussion
9.7 Recommendations
9.8 Notes and references
Exercises
10 Clustering
10.1
10.2
10.3
10.4
10.5
Introduction
Hierarchical methods
10.2.1 Single-link method
10.2.2 Complete-link method
10.2.3 Sum-of-squares method
10.2.4 General agglomerative algorithm
10.2.5 Properties of a hierarchical classification
10.2.6 Example application study
10.2.7 Summary
Quick partitions
Mixture models
10.4.1 Model description
10.4.2 Example application study
Sum-of-squares methods
10.5.1 Clustering criteria
10.5.2 Clustering algorithms
10.5.3 Vector quantisation
307
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CONTENTS xiii
10.5.4 Example application study 394
10.5.5 Further developments 395
10.5.6 Summary 395
10.6 Cluster validity 396
10.6.1 Introduction 396
10.6.2 Distortion measures 397
10.6.3 Choosing the number of clusters 397
10.6.4 Identifying genuine clusters 399
10.7 Application studies 400
10.8 Summary and discussion 402
10.9 Recommendations 404
10.10 Notes and references 405
Exercises 406
11 Additional topics 409
11.1 Model selection 409
11.1.1 Separate training and test sets 410
11.1.2 Cross-validation 410
11.1.3 The Bayesian viewpoint 411
11.1.4 Akaike s information criterion 411
11.2 Learning with unreliable classification 412
11.3 Missing data 413
11.4 Outlier detection and robust procedures 414
11.5 Mixed continuous and discrete variables 415
11.6 Structural risk minimisation and the Vapnik-Chervonenkis
dimension
11.6.1 Bounds on the expected risk
11.6.2 The Vapnik-Chervonenkis dimension
A Measures of dissimilarity
A.l Measures of dissimilarity
A. 1.1 Numeric variables
A. 1.2 Nominal and ordinal variables
A.1.3 Binary variables
A. 1.4 Summary
A.2 Distances between distributions
A.2.1 Methods based on prototype vectors
A.2.2 Methods based on probabilistic distance
A.2.3 Probabilistic dependence
A.3 Discussion
B Parameter estimation
B.l Parameter estimation
B.l.l Properties of estimators
B.1.2 Maximum likelihood
B.1.3 Problems with maximum likelihood
B.1.4 Bayesian estimates
416
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xiv CONTENTS
C Linear algebra 437
C. 1 Basic properties and definitions 437
C.2 Notes and references 441
D Data 443
D. 1 Introduction 443
D.2 Formulating the problem 443
D.3 Data collection 444
D.4 Initial examination of data 446
D.5 Data sets 448
D.6 Notes and references 448
E Probability theory 449
E.l Definitions and terminology 449
E.2 Normal distribution 454
E.3 Probability distributions 455
References 459
Index 49I
|
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| author | Webb, Andrew R. |
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| classification_rvk | SK 850 |
| ctrlnum | (OCoLC)248278301 (DE-599)BVBBV017935195 |
| dewey-full | 006.4 |
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| 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 Mathematik |
| edition | 2. ed., repr. |
| format | Book |
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| publisher | Wiley |
| record_format | marc |
| spelling | Webb, Andrew R. Verfasser (DE-588)140273085 aut Statistical pattern recognition Andrew R. Webb 2. ed., repr. Chichester Wiley 2003 XVIII, 496 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Klassifikation (DE-588)4030958-7 gnd rswk-swf Statistische Analyse (DE-588)4116599-8 gnd rswk-swf Bildauswertung (DE-588)4145394-3 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Bildauswertung (DE-588)4145394-3 s Mustererkennung (DE-588)4040936-3 s Statistik (DE-588)4056995-0 s Klassifikation (DE-588)4030958-7 s 1\p DE-604 Statistische Analyse (DE-588)4116599-8 s 2\p DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010753810&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 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Webb, Andrew R. Statistical pattern recognition Klassifikation (DE-588)4030958-7 gnd Statistische Analyse (DE-588)4116599-8 gnd Bildauswertung (DE-588)4145394-3 gnd Mustererkennung (DE-588)4040936-3 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4030958-7 (DE-588)4116599-8 (DE-588)4145394-3 (DE-588)4040936-3 (DE-588)4056995-0 |
| title | Statistical pattern recognition |
| title_auth | Statistical pattern recognition |
| title_exact_search | Statistical pattern recognition |
| title_full | Statistical pattern recognition Andrew R. Webb |
| title_fullStr | Statistical pattern recognition Andrew R. Webb |
| title_full_unstemmed | Statistical pattern recognition Andrew R. Webb |
| title_short | Statistical pattern recognition |
| title_sort | statistical pattern recognition |
| topic | Klassifikation (DE-588)4030958-7 gnd Statistische Analyse (DE-588)4116599-8 gnd Bildauswertung (DE-588)4145394-3 gnd Mustererkennung (DE-588)4040936-3 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Klassifikation Statistische Analyse Bildauswertung Mustererkennung Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010753810&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT webbandrewr statisticalpatternrecognition |