Supergraph models: a novel approach for structure learning, classification and recognition
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Münster
Verl.-Haus Monsenstein und Vannerdat
2013
|
| Schriftenreihe: | MV Wissenschaft
|
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | XVII, 185 S. Ill., graph. Darst. |
| ISBN: | 9783869918259 386991825X |
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Datensatz im Suchindex
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IMAGE 1
C O N T E N T S
LIST O F T A B L E S X I I I
LIST O F F I G U R E S X V
1 I N T R O D U C T I O N 1
1.1 S T R U C T U R E RECOGNITION 4
1.2 GOAL A N D PROBLEM STATEMENT. 7
1.3 SHORT S U M M A R Y OF RESULTS 8
1.4 PREVIEW OF THESIS WORK 10
2 R E L A T E D W O R K 1 1
2.1 S T R U C T U R E MODEL LEARNING 13
2.1.1 BLOCKS WORLD 13
2.1.2 VERSION SPACE LEARNING 14
2.1.3 S U B D U E A N D S U B D U E C L 15
2.1.4 INDUCTIVE LOGIC P R O G R A M M I N G 16
2.2 S T R U C T U R E MODELLING 17
2.2.1 GRAPHICAL MODELS 17
2.2.1.1 MARKOV R A N D O M FIELDS 18
2.2.1.2 BAYESIAN COMPOSITIONAL HIERARCHIES 11)
2.2.1.3 HIDDEN MARKOV MODELS 20
2.2.1.4 S T R U C T U R E LEARNING FOR GRAPHICAL MODELS 20
2.2.2 CONTEXT FREE G R A M M A R S 21
2.3 S T R U C T U R E CLASSIFICATION 22
2.3.1 G R A P H KERNELS 22
2.3.2 KERNEL S U P P O R T VECTOR MACHINES 23
2.3.3 RECURRENT NEURAL NETWORKS 23
2.4 SCENE INTERPRETATION SYSTEMS 25
2.4.1 CRISP INTERPRETATION 25
2.4.2 HYBRID INTERPRETATION 2T
2.4.2.1 S C E N I C 27
2.4.2.2 SCENIOR 28
2.4.2.3 STOCHASTIC G R A M M A R S 30
2.5 S U M M A R Y 30
3 S U P E R G R A P H M O D E L S 3 3
3.1 MODEL REPRESENTATION 33
3.1.1 S T R U C T U R E G R A P H 34
I X
HTTP://D-NB.INFO/1033300454
IMAGE 2
C O N T E N T S
3.1.1.1 A T T R I B U T E VALUE DOMAINS 37
SYMBOLIC DOMAINS 38
RANGE DOMAINS 3!)
3 .1 .1.2 MAXIMUM C O M M O N GCNERALISABLE SUBGRAPH 39
3.1.1.3 STRUCTURE G R A P H CLASSIFICATION 42
3.1.2 OBSERVATION HISTOGRAM 42
3.1.2.1 EXACT OBSERVATIONS 45
3.1.2.2 GENERALISED OBSERVATIONS 4(I
3.1.2.3 COMPARISON A N D DISCUSSION 49
3.1.2.4 EVALUATION ON SYNTHETIC D A T A 53
3.1.2.5 MODEL PRIORS 54
3.1.2.(I LIKELIHOOD MAXIMISATION 55
3.1.3 MODEL DEFINITION A N D S U M M A R Y 56
3.2 MODEL LEARNING 56
3.2.1 OUTLINE 57
3.2.2 A T T R I B U T E GENERALISATION COST FUNCTION 58
3.2.3 STRUCTURE G R A P H GENERALISATION 5!)
3.2.4 A T T R I B U T E VALUE GENERALISATION (IL
3.2.5 OBSERVATION HISTOGRAM U P D A T E 62
3.3 LEARNING FROM NEGATIVE EXAMPLES 62
3.4 INDUCTIVE BIAS 64
3.5 COMPLEXITY ANALYSIS A N D SCALABILITY 65
3.6 EVALUATION OF OPTIMALITV 66
3.7 (LOSSLESS) ATTRIBUTED G R A P H COMPRESSION 67
3.8 S U M M A R Y A N D CONCLUSIONS 68
4 A P P L I C A T I O N O F L E A R N T M O D E L S 7 1
4.1 M A X I M U M A-POSTERIORI PROBABILITY CLASSIFICATION OF O B J E C T
STRUCTURES 72
4.2 CLUSTERING OF OBJECT STRUCTURES 73
4.3 MULTI-CLASSIFIC ATION SCENARIOS 75
4.3.1 MULTI-MODEL SCALING 76
4.3.2 ONE-SHOT LEARNING EXPERIMENTS 78
4.4 RECOGNITION OF OBJECT STRUCTURES 7!)
4.4.1 PROBLEM DEFINITION 82
4.4.2 OUTLINE 83
4.4.3 PROBABILISTIC FRAMEWORK 85
4.4.4 BOTTOM-UP COMPOSITIONAL S T R U C T U R E RECOGNITION 86
4.4.5 COMPLEXITY ANALYSIS 87
4.4.6 DATA-INDEPENDENT, SEARCH SPACE OPTIMISATION 88
4.4.6.1 LEVEHVISE OPTIMISATION 88
4.4.6.2 GLOBAL OPTIMISATION 8!)
4.4.7 DATA-DEPENDENT SEARCH SPACE OPTIMISATION 8!)
4.4.7.1 ETR.IMS DATASET 90
4.4.7.2 CONLL DATASET; 90
4.4.8 STRUCTURE RECOGNITION SCORE 90
IMAGE 3
C O N T E N T S XI
5 E X P E R I M E N T A L E V A L U A T I O N 9 3
* R .L BENCHMARK DATA.SET,S 93
5.1.1 ETR.IMS S A M E INTERPRETATION DATA,SET 94
5.1.1.1 D A T A DESCRIPTION 95
5.1.1.2 A T T R I B U T E VALUE DOMAINS !)!)
5.1.2 M U T A G MUTAGENICITY PREDICTION DATASET 101
5.1.2.1 D A T A DESCRIPTION 101
5.1.2.2 A T T R I B U T E VALUE DOMAINS 102
5.1.3 CONLL TEXT CHUNKING DATASET 103
5.1.3.1 D A T A DESCRIPTION 104
5.1.3.2 A T T R I B U T E VALUE DOMAINS 105
5.1.4 C O F R I E N D AIRPORT, SERVICE EVENT DATASET 107
5.1.4.1 D A T A DESCRIPTION 108
5.1.4.2 A T T R I B U T E VALUE DOMAINS 10!)
5.2 S T R U C T U R E CLASSIFICATION EXPERIMENTS 113
5.2.1 CLASSIFICATION OF FACADE STRUCTURES 114
5.2.1.1 ONE-SHOT. LEARNING 118
5.2.1.2 DISCUSSION 119
5.2.2 CLASSIFICATION OF MOLECULES 120
5.2.2.1 ONE-SHOT. LEARNING 123
5.2.2.2 DISCUSSION 124
5.2.3 CLASSIFICATION OF TEXT CHUNKS 125
5.2.3.1 DISCUSSION 129
5.2.4 CLASSIFICATION OF AIRPORT, SERVICE ACTIONS 129
5.2.4.1 DISCUSSION 132
5.3 STRUCTURE; RECOGNITION EXPERIMENTS 133
5.3.1 RECOGNITION OF FACADE STRUCTURES 133
5.3.1.1 DISCUSSION 148
5.3.2 RECOGNITION OF TEXT CHUNKS 149
5.3.2.1 DISCUSSION 154
5.3.3 RECOGNITION OF AIRPORT SERVICE ACTIONS 155
5.4 S T R U C T U R E PREDICTION EXPERIMENTS 15(5
5.4.1 PREDICTION OF MOLECULES 15(5
5.4.1.1 DISCUSSION 159
5.5 S U M M A R Y 1( 1
6 C O N C L U S I O N S 1 6 5
FI.L S U M M A R Y OF RESULTS LF (
(I.2 O P E N ISSUES 1(58
(I.3 COMPARISON T O O T H E R WORK 1( 9
FT.4 FURTHER WORK 170
6.5 OUTLOOK 171
B I B L I O G R A P H Y 1 7 3 |
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| discipline | Informatik Mathematik |
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| spelling | Hartz, Johannes Verfasser aut Supergraph models a novel approach for structure learning, classification and recognition by Johannes Hartz Münster Verl.-Haus Monsenstein und Vannerdat 2013 XVII, 185 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier MV Wissenschaft Hamburg, Univ., Diss., 2012 Graphisches Modell (DE-588)4606156-3 gnd rswk-swf Automatische Klassifikation (DE-588)4120957-6 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf Strukturlernen (DE-588)4691220-4 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Strukturlernen (DE-588)4691220-4 s Mustererkennung (DE-588)4040936-3 s Automatische Klassifikation (DE-588)4120957-6 s Graphisches Modell (DE-588)4606156-3 s DE-604 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=4294830&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026188038&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hartz, Johannes Supergraph models a novel approach for structure learning, classification and recognition Graphisches Modell (DE-588)4606156-3 gnd Automatische Klassifikation (DE-588)4120957-6 gnd Mustererkennung (DE-588)4040936-3 gnd Strukturlernen (DE-588)4691220-4 gnd |
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| title | Supergraph models a novel approach for structure learning, classification and recognition |
| title_auth | Supergraph models a novel approach for structure learning, classification and recognition |
| title_exact_search | Supergraph models a novel approach for structure learning, classification and recognition |
| title_full | Supergraph models a novel approach for structure learning, classification and recognition by Johannes Hartz |
| title_fullStr | Supergraph models a novel approach for structure learning, classification and recognition by Johannes Hartz |
| title_full_unstemmed | Supergraph models a novel approach for structure learning, classification and recognition by Johannes Hartz |
| title_short | Supergraph models |
| title_sort | supergraph models a novel approach for structure learning classification and recognition |
| title_sub | a novel approach for structure learning, classification and recognition |
| topic | Graphisches Modell (DE-588)4606156-3 gnd Automatische Klassifikation (DE-588)4120957-6 gnd Mustererkennung (DE-588)4040936-3 gnd Strukturlernen (DE-588)4691220-4 gnd |
| topic_facet | Graphisches Modell Automatische Klassifikation Mustererkennung Strukturlernen Hochschulschrift |
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