Markov Models for Pattern Recognition: from theory to applications
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
London [u.a.]
Springer
2014
|
| Edition: | 2. ed. |
| Series: | Advances in Computer Vision and Pattern Recognition
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XIII, 276 S. Ill., graph. Darst. |
| ISBN: | 9781447163077 |
Staff View
MARC
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Record in the Search Index
| _version_ | 1804152072613920768 |
|---|---|
| adam_text | Titel: Markov models for pattern recognition
Autor: Fink, Gernot A
Jahr: 2014
Contents
1 Introduction..............................................................1
1.1 Thematic Context..................................................3
1.2 Functional Principles of Markov Models..........................4
1.3 Goal and Structure of the Book....................................5
2 Application Areas........................................................9
2.1 Speech..............................................................9
2.2 Writing..............................................................15
2.3 Biological Sequences..............................................24
2.4 Outlook ............................................................28
Part I Theory
3 Foundations of Mathematical Statistics................................35
3.1 Random Experiment, Event, and Probability......................35
3.2 Random Variables and Probability Distributions..................37
3.3 Parameters of Probability Distributions............................39
3.4 Normal Distributions and Mixture Models........................41
3.5 Stochastic Processes and Markov Chains..........................42
3.6 Principles of Parameter Estimation................................44
3.6.1 Maximum Likelihood Estimation..........................44
3.6.2 Maximum a Posteriori Estimation..........................47
3.7 Bibliographical Remarks ..........................................49
4 Vector Quantization and Mixture Estimation........................51
4.1 Definition ..........................................................52
4.2 Optimality..........................................................53
4.2.1 Nearest-Neighbor Condition................................54
4.2.2 Centroid Condition ........................................55
4.3 Algorithms for Vector Quantizer Design..........................57
4.3.1 Lloyd s Algorithm..........................................57
4.3.2 LBG Algorithm............................................59
ix
X Contents
4.3.3 fc-Means Algorithm........................................61
4.4 Estimation of Mixture Density Models............................62
4.4.1 EM Algorithm..............................................63
4.4.2 EM Algorithm for Gaussian Mixtures......................66
4.5 Bibliographical Remarks ..........................................69
5 Hidden Markov Models................................................71
5.1 Definition ..........................................................72
5.2 Modeling Outputs..................................................73
5.3 Use Cases..........................................................75
5.4 Notation............................................................78
5.5 Evaluation..........................................................78
5.5.1 The Total Output Probability ..............................79
5.5.2 Forward Algorithm ........................................80
5.5.3 The Optimal Output Probability............................82
5.6 Decoding............................................................85
5.6.1 Viterbi Algorithm..........................................86
5.7 Parameter Estimation ..............................................87
5.7.1 Foundations................................................88
5.7.2 Forward-Backward Algorithm............................89
5.7.3 Training Methods..........................................90
5.7.4 Baum-Welch Algorithm....................................92
5.7.5 Viterbi Training............................................96
5.7.6 Segmental ¿-Means Algorithm............................100
5.7.7 Multiple Observation Sequences ..........................103
5.8 Model Variants......................................................104
5.8.1 Alternative Algorithms ....................................104
5.8.2 Alternative Model Architectures ..........................104
5.9 Bibliographical Remarks ..........................................105
6 n-Gram Models..........................................................107
6.1 Definition ..........................................................107
6.2 Use Cases..........................................................109
6.3 Notation............................................................110
6.4 Evaluation..........................................................110
6.5 Parameter Estimation ..............................................114
6.5.1 Redistribution of Probability Mass........................115
6.5.2 Discounting................................................115
6.5.3 Incorporation of More General Distributions..............117
6.5.4 Interpolation................................................118
6.5.5 Backing off ................................................120
6.5.6 Optimization of Generalized Distributions................121
6.6 Model Variants......................................................123
6.6.1 Category-Based Models....................................123
6.6.2 Longer Temporal Dependencies............................126
6.7 Bibliographical Remarks ..........................................126
Contents x¡
Part II Practice
7 Computations with Probabilities......................................133
7.1 Logarithmic Probability Representation............................134
7.2 Lower Bounds for Probabilities....................................137
7.3 Codebook Evaluation for Semi-continuous HMMs................138
7.4 Probability Ratios..................................................139
8 Configuration of Hidden Markov Models ............................143
8.1 Model Topologies..................................................143
8.2 Modularization......................................................144
8.2.1 Context-Independent Sub-word Units......................145
8.2.2 Context-Dependent Sub-word Units........................146
8.3 Compound Models..................................................147
8.4 Profile HMMs......................................................149
8.5 Modeling Outputs..................................................151
9 Robust Parameter Estimation..........................................153
9.1 Feature Optimization ..............................................155
9.1.1 Decorrelation ..............................................156
9.1.2 Principal Component Analysis I............................157
9.1.3 Whitening..................................................162
9.1.4 Dimensionality Reduction..................................163
9.1.5 Principal Component Analysis II..........................164
9.1.6 Linear Discriminant Analysis..............................165
9.2 Tying................................................................169
9.2.1 Sub-model Units............................................170
9.2.2 State Tying..................................................174
9.2.3 Tying in Mixture Models ..................................178
9.3 Initialization of Parameters........................................181
10 Efficient Model Evaluation..............................................183
10.1 Efficient Evaluation of Mixture Densities..........................184
10.2 Efficient Decoding of Hidden Markov Models....................185
10.2.1 Beam Search Algorithm....................................186
10.3 Efficient Generation of Recognition Results ......................189
10.3.1 First-Best Decoding of Segmentation Units................189
10.3.2 Algorithms for N-Best Search..............................190
10.4 Efficient Parameter Estimation ....................................192
10.4.1 Forward-Backward Pruning................................192
10.4.2 Segmental Baum-Welch Algorithm........................193
10.4.3 Training of Model Hierarchies ............................194
10.5 Tree-Like Model Organization ....................................195
10.5.1 HMM Prefix Trees..........................................195
10.5.2 Tree-Like Representation for n-Gram Models............197
xü Contents
11 Model Adaptation ......................................................201
11.1 Basic Principles....................................................201
11.2 Adaptation of Hidden Markov Models............................202
11.2.1 Maximum-Likelihood Linear-Regression..................204
11.3 Adaptation of n-Gram Models......................................207
11.3.1 Cache Models..............................................207
11.3.2 Dialog-Step Dependent Models............................208
11.3.3 Topic-Based Language Models............................208
12 Integrated Search Methods............................................211
12.1 HMM Networks....................................................215
12.2 Multi-pass Search..................................................216
12.3 Search Space Copies................................................217
12.3.1 Context-Based Search Space Copies......................218
12.3.2 Time-Based Search Space Copies..........................219
12.3.3 Language-Model Look-Ahead..............................220
12.4 Time-Synchronous Parallel Model Decoding......................222
12.4.1 Generation of Segment Hypotheses........................222
12.4.2 Language-Model-Based Search............................223
Part III Systems
13 Speech Recognition......................................................229
13.1 Recognition System of RWTH Aachen University................229
13.1.1 Feature Extraction..........................................230
13.1.2 Acoustic Modeling..........................................230
13.1.3 Language Modeling........................................231
13.1.4 Search......................................................231
13.2 BBN Speech Recognizer BYBLOS................................231
13.2.1 Feature Extraction..........................................232
13.2.2 Acoustic Modeling..........................................232
13.2.3 Language Modeling........................................232
13.2.4 Search......................................................233
13.3 ESMERALDA......................................................233
13.3.1 Feature Extraction..........................................234
13.3.2 Acoustic Modeling..........................................235
13.3.3 Statistical and Declarative Language Modeling............235
13.3.4 Incremental Search ........................................236
14 Handwriting Recognition ..............................................237
14.1 Recognition System by BBN......................................238
14.1.1 Preprocessing..............................................238
14.1.2 Feature Extraction..........................................238
14.1.3 Script Modeling............................................239
14.1.4 Language Modeling and Search............................240
14.2 Recognition System of RWTH Aachen University................240
14.2.1 Preprocessing ..............................................240
Contents xü¡
14.2.2 Feature Extraction..........................................241
14.2.3 Script Modeling............................................241
14.2.4 Language Modeling and Search............................242
14.3 ESMERALDA Offline Recognition System........................242
14.3.1 Preprocessing..............................................242
14.3.2 Feature Extraction..........................................243
14.3.3 Handwriting Model........................................244
14.3.4 Language Modeling and Search............................244
14.4 Bag-of-Features Hidden Markov Models..........................244
15 Analysis of Biological Sequences ............................249
15.1 HMMER............................................................250
15.1.1 Model Structure............................................250
15.1.2 Parameter Estimation......................................250
15.1.3 Interoperability ............................................251
15.2 SAM................................................................251
15.3 ESMERALDA......................................................252
15.3.1 Feature Extraction..........................................252
15.3.2 Statistical Models of Proteins..............................253
References.................................. 255
Index..................................... 273
|
| any_adam_object | 1 |
| author | Fink, Gernot A. 1965- |
| author_GND | (DE-588)128427086 |
| author_facet | Fink, Gernot A. 1965- |
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| ctrlnum | (OCoLC)881299724 (DE-599)BVBBV041765883 |
| discipline | Informatik |
| edition | 2. ed. |
| format | Book |
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| id | DE-604.BV041765883 |
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| indexdate | 2024-07-10T01:04:54Z |
| institution | BVB |
| isbn | 9781447163077 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027211976 |
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| physical | XIII, 276 S. Ill., graph. Darst. |
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| series2 | Advances in Computer Vision and Pattern Recognition |
| spelling | Fink, Gernot A. 1965- Verfasser (DE-588)128427086 aut Markov Models for Pattern Recognition from theory to applications Gernot A. Fink 2. ed. London [u.a.] Springer 2014 XIII, 276 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Advances in Computer Vision and Pattern Recognition Markov-Kette (DE-588)4037612-6 gnd rswk-swf Hidden-Markov-Modell (DE-588)4352479-5 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Computersimulation (DE-588)4148259-1 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 s Mustererkennung (DE-588)4040936-3 s Computersimulation (DE-588)4148259-1 s Markov-Kette (DE-588)4037612-6 s DE-604 Hidden-Markov-Modell (DE-588)4352479-5 s 1\p DE-604 Erscheint auch als Online-Ausgabe 978-1-4471-6308-4 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027211976&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 |
| spellingShingle | Fink, Gernot A. 1965- Markov Models for Pattern Recognition from theory to applications Markov-Kette (DE-588)4037612-6 gnd Hidden-Markov-Modell (DE-588)4352479-5 gnd Mustererkennung (DE-588)4040936-3 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Computersimulation (DE-588)4148259-1 gnd |
| subject_GND | (DE-588)4037612-6 (DE-588)4352479-5 (DE-588)4040936-3 (DE-588)4033447-8 (DE-588)4148259-1 |
| title | Markov Models for Pattern Recognition from theory to applications |
| title_auth | Markov Models for Pattern Recognition from theory to applications |
| title_exact_search | Markov Models for Pattern Recognition from theory to applications |
| title_full | Markov Models for Pattern Recognition from theory to applications Gernot A. Fink |
| title_fullStr | Markov Models for Pattern Recognition from theory to applications Gernot A. Fink |
| title_full_unstemmed | Markov Models for Pattern Recognition from theory to applications Gernot A. Fink |
| title_short | Markov Models for Pattern Recognition |
| title_sort | markov models for pattern recognition from theory to applications |
| title_sub | from theory to applications |
| topic | Markov-Kette (DE-588)4037612-6 gnd Hidden-Markov-Modell (DE-588)4352479-5 gnd Mustererkennung (DE-588)4040936-3 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Computersimulation (DE-588)4148259-1 gnd |
| topic_facet | Markov-Kette Hidden-Markov-Modell Mustererkennung Künstliche Intelligenz Computersimulation |
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