Pattern recognition principles:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Reading, Mass. u.a.
Addison-Wesley
1981
|
| Ausgabe: | 4. print. |
| Schriftenreihe: | Applied mathematics and computation
7 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXII, 377 S. zahlr. Ill. u. graph. Darst. |
| ISBN: | 0201075873 |
Internformat
MARC
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| 245 | 1 | 0 | |a Pattern recognition principles |c Julius T. Tou ; Rafael C. Gonzalez |
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Datensatz im Suchindex
| _version_ | 1804123678016798720 |
|---|---|
| adam_text | Titel: Pattern recognition principles
Autor: Tou, Julius T.
Jahr: 1981
CONTENTS
Series Editor s Foreword.............. xiii
Preface...................... xv
Notation..................... xix
Chapter 1 Introduction
1.1 The Information-Handling Problem.......... 1
1.2 Basic Concepts of Pattern Recognition......... 5
1.3 Fundamental Problems in Pattern Recognition System
Design....................... 9
1.4 Design Concepts and Methodologies.......... 17
1.5 Examples of Automatic Pattern Recognition Systems . . 21
1.6 A Simple Automatic Pattern Recognition Model..... 36
Chapter 2 Decision Functions
2.1 Introduction.................... 39
2.2 Linear Decision Functions.............. 40
2.3 Generalized Decision Functions............ 48
2.4 Pattern Space and Weight Space........... 53
2.5 Geometrical Properties................ 55
2.5.1 Hyperplane Properties............. 55
2.5.2 Dichotomies................. 58
2.5.3 Dichotomization Capacity of Generalized Decision
Functions.................. 60
2.6 Implementation of Decision Functions......... 62
2.7 Functions of Several Variables............ 64
2.7.1 Definitions.................. 65
?? CONTENTS
2.7.2 Construction of Multivariate Functions...... 67
2.7.3 Orthogonal and Orthonormal Systems of Functions 68
2.8 Concluding Remarks................. 72
References..................... 73
Problems...................... 73
Chapter 3 Pattern Classification by Distance Functions
3.1 Introduction.................... 75
3.2 Minimum-Distance Pattern Classification........ 76
3.2.1 Single Prototypes............... 77
3.2.2 Multiprototypes................ 78
3.2.3 Extension of Minimum-Distance Classification
Concepts................... 81
3.2.4 A Design Example.............. 83
3.3 Cluster Seeking................... 86
3.3.1 Measures of Similarity............. 87
3.3.2 Clustering Criteria............... 89
3.3.3 A Simple Cluster-Seeking Algorithm....... 90
3.3.4 Maximin-Distance Algorithm.......... 92
3.3.5 K-Means Algorithm.............. 94
3.3.6 Isodata Algorithm............... 97
3.3.7 Evaluation of Clustering Results........ 104
3.3.8 Graph-Theoretic Approach........... 106
3.4 Unsupervised Pattern Recognition........... 107
3.5 Concluding Remarks................. 108
References..................... 108
Problems...................... 109
Chapter 4 Pattern Classification by Likelihood Functions
4.1 Introduction.................... 110
4.2 Pattern Classification as a Statistical Decision Problem . . Ill
4.3 Bayes Classifier for Normal Patterns.......... 119
4.4 Error Probabilities.................. 124
4.5 A Family of Important Probability Density Functions . . 130
4.Í) Estimation of Probability Density Functions...... 134
4.6.1 Form of the Probability Density Function .... 134
4.6.2 Estimation of the Mean Vector and Covariance
Matrix........^............ 137
4.6.3 Bayesian Learning of the Mean Vector and
Covariance Matrix............... 139
CONTENTS ix
4.6.4 Functional Approximation of Probability Density
Functions.................. 145
4.7 Concluding Remarks................. 154
References..................... 155
Problems...................... 156
Chapter 5 Trainable Pattern Classifiers—The Deterministic
Approach
5.1 Introduction.................... 158
5.2 The Perceptron Approach.............. 159
5.2.1 The Reward-Punishment Concept........ 161
5.2.2 Proof of Convergence............. 165
5.2.3 Variations of the Perceptron Approach...... 168
5.3 Derivation of Pattern Classification Algorithms..... 169
5.3.1 The Gradient Technique............ 169
5.3.2 Perceptron Algorithm............. 171
5.3.3 A Least-Mean-Square-Error Algorithm...... 173
5.3.4 Convergence Proof of the LMSE Algorithm .... 178
5.4 Multicategory Classification.............. 181
5.5 Learning and Generalization............. 186
5.6 The Potential Function Approach........... 187
5.6.1 Generation of Decision Functions........ 188
5.6.2 Selection of Potential Functions......... 193
5.6.3 Geometrical Interpretation and Weight Adjustment 202
5.6.4 Convergence of Training Algorithms....... 208
5.6.5 Multiclass Generalization............ 212
5.7 Concluding Remarks................. 213
References..................... 214
Problems...................... 215
Chapter 6 Trainable Pattern Classifiers—The Statistical Approach
6.1 Introduction.................... 217
6.2 Stochastic Approximation Methods.......... 218
6.2.1 The Robbins-Monro Algorithm......... 219
6.2.2 Speed of Convergence............. 224
6.2.3 Multidimensional Extension........... 225
6.3 Derivation of Pattern Classification Algorithms..... 226
6.3.1 Estimation of Optimum Decision Functions by
Stochastic Approximation Methods....... 226
6.3.2 Increment-Correction Algorithm......... 229
? CONTENTS
6.3.3 Least-Mean-Square-Error Algorithm....... 233
6.4 The Method of Potential Functions.......... 235
6.5 Concluding Remarks................. 239
References..................... 240
Problems...................... 241
Chapter 7 Pattern Preprocessing and Feature Selection
7.1 Introduction.................... 243
7.2 Distance Measures.................. 247
7.3 Clustering Transformations and Feature Ordering .... 250
7.4 Clustering in Feature Selection............ 259
7.5 Feature Selection Through Entropy Minimization .... 263
7.6 Feature Selection Through Orthogonal Expansions. . . . 269
7.6.1 Review of the Fourier Series Expansion..... 269
7.6.2 Karhunen-Loève Expansion.......... 271
7.7 Feature Selection Through Functional Approximation . . 283
7.7.1 Functional Expansion............. 283
7.7.2 Stochastic Approximation Formulation...... 288
7.7.3 Kernel Approximation............. 290
7.7.4 Use of Feature Functions in Classification .... 291
7.8 Divergence Concept................. 291
7.9 Feature Selection Through Divergence Maximization . . . 298
7.10 Binary Feature Selection............... 307
7.10.1 A Sequential Algorithm............ 308
7.10.2 A Parallel Algorithm.............. 310
7.11 Concluding Remarks................. 313
References..................... 314
Problems...................... 314
Chapter 8 Syntactic Pattern Recognition
8.1 Introduction.................... 316
8.2 Concepts From Formal Language Theory........ 317
8.2.1 Definitions.................. 317
8.2.2 Types of Grammars.............. 320
8.3 Formulation of the Syntactic Pattern Recognition Problem 323
8.4 Syntactic Pattern Description............. 324
8.5 Recognition Grammars................ 328
8.5.1 Syntax-Directed Recognition.......... 328
8.5.2 Recognition of Graph-Like Patterns....... 331
8.5.3 Recognition of Tree Structures......... 339
CONTENTS Xi
8.6 Statistical Considerations............... 340
8.6.1 Stochastic Grammars and Languages...... 341
8.6.2 Learning the Production Probabilities...... 343
8.7 Learning and Grammatical Inference.......... 350
8.7.1 Inference of String Grammars.......... 350
8.7.2 Inference of Two-Dimensional Grammars..... 355
8.8 Automata as Pattern Recognizers........... 357
8.9 Concluding Remarks................. 360
References..................... 361
Problems...................... 361
Bibliography................... 363
Index....................... 372
|
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| indexdate | 2024-07-09T17:33:35Z |
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| isbn | 0201075873 |
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| series | Applied mathematics and computation |
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| spelling | Tou, Julius T. 1926- Verfasser (DE-588)172419921 aut Pattern recognition principles Julius T. Tou ; Rafael C. Gonzalez 4. print. Reading, Mass. u.a. Addison-Wesley 1981 XXII, 377 S. zahlr. Ill. u. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Applied mathematics and computation 7 Mustererkennung (DE-588)4040936-3 gnd rswk-swf Zeichenerkennung (DE-588)4067445-9 gnd rswk-swf 1\p (DE-588)4151278-9 Einführung gnd-content Mustererkennung (DE-588)4040936-3 s DE-604 Zeichenerkennung (DE-588)4067445-9 s 2\p DE-604 Gonzalez, Rafael C. Verfasser aut Applied mathematics and computation 7 (DE-604)BV001890279 7 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=006141387&sequence=000002&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 | Tou, Julius T. 1926- Gonzalez, Rafael C. Pattern recognition principles Applied mathematics and computation Mustererkennung (DE-588)4040936-3 gnd Zeichenerkennung (DE-588)4067445-9 gnd |
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| title | Pattern recognition principles |
| title_auth | Pattern recognition principles |
| title_exact_search | Pattern recognition principles |
| title_full | Pattern recognition principles Julius T. Tou ; Rafael C. Gonzalez |
| title_fullStr | Pattern recognition principles Julius T. Tou ; Rafael C. Gonzalez |
| title_full_unstemmed | Pattern recognition principles Julius T. Tou ; Rafael C. Gonzalez |
| title_short | Pattern recognition principles |
| title_sort | pattern recognition principles |
| topic | Mustererkennung (DE-588)4040936-3 gnd Zeichenerkennung (DE-588)4067445-9 gnd |
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