Machine learning of timed automata:
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
2013
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| Schlagworte: | |
| Online-Zugang: | Volltext https://nbn-resolving.org/urn:nbn:de:bvb:91-diss-20131216-1145664-0-4 Inhaltsverzeichnis |
| Beschreibung: | X, 179 S. graph. Darst. |
Internformat
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| 245 | 1 | 0 | |a Machine learning of timed automata |c Jana A. Schmidt |
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Datensatz im Suchindex
| _version_ | 1804151748649025536 |
|---|---|
| adam_text | CONTENTS
1 INTRODUCTION 1
1.1 MOTIVATION 1
1.2 ORGANIZATION OF THE WORK 3
2 RELATED WORK 7
2.1 GRAPHICAL CAUSAL MODELS 8
2.1.1 CAUSAL NETWORKS 9
2.1.2 BAYESIAN NETWORKS 10
2.1.3 MARKOV RANDOM FIELDS 11
2.1.4 FACTOR GRAPHS 13
2.2 PROCESS MINING 14
2.2.1 HIDDEN MARKOV MODELS 14
2.2.2 DYNAMIC BAYESIAN NETS 17
2.2.3 PETRI NETS 18
2.3 AUTOMATA 20
2.3.1 GRAMMATICAL INFERENCE 20
2.3.2 AUTOMATA INDUCTION 23
2.4 APPLICABILITY TO THE PROBLEM SETTING 31
2.4.1 COMBINING THE BEST OF THE PRESENTED APPROACHES ... 32
3 PRELIMINARIES: MATERIALS AND QUALITY MEASURES 33
3.1 DATA 33
3.1.1 SYNTHETIC DATA 33
3.1.2 REAL WORLD DATA SETS 35
DISEASE GROUP DATA SET I 35
DISEASE GROUP DATA SET II 35
HEPATITIS DATA SET 36
YEAST METABOLISM DATA 36
ZOO DATA SET 37
3.2 QUALITY MEASURES 38
VII
HTTP://D-NB.INFO/1047906457
VIII CONTENTS
4 LEARNING PRTAS FROM MULTI-ATTRIBUTE EVENT LOGS (PRTA) 41
4.1 PROBABILISTIC REAL-TIME AUTOMATA 41
4.1.1 ACCEPTING WORDS 43
DEFINITION OF WORDS 43
SOLVING THE WORD PROBLEM 44
4.1.2 INDUCTION OF A PRTA 45
4.1.3 PREDICTING WITH AN AUTOMATON 50
4.2 EXPERIMENTAL RESULTS 51
4.2.1 DISTANCE MEASURE FOR MEDICAL APPLICATIONS 51
4.2.2 RESULTS 52
4.2.3 PROOF OF CONCEPT 53
4.2.4 EXTRACTION OF AN AUTOMATON ON REAL WORLD DATA ... 54
4.2.5 EMPIRICAL EVALUATION 62
CUT-OFF DEPENDENCIES 62
DEPENDENCY ON THE NUMBER OF INPUT SEQUENCES .... 63
RUNTIME EVALUATION 64
4.2.6 COMPARISON WITH AN MULTI-OUTPUT HMM 64
4.2.7 COMPARISON WITH PROCESS MINING ALGORITHMS 65
4.3 CONCLUSION 68
5 SCALABLE INDUCTION OF PROBABILISTIC REAL TIME AUTOMATA
(SPRTA) 71
5.1 ALGORITHM 72
5.1.1 PROBLEM SETTING 72
5.1.2 BASIC ALGORITHM 73
5.1.3 FINDING THE BEST SUITED CLUSTER 76
PRELIMINARIES 76
A DECISION FUNCTION FOR THE ONLINE CLUSTERING .... 77
EXAMPLE 78
5.1.4 POSTPROCESSING 79
5.2 EXPERIMENTS 79
5.2.1 PERFORMANCE ON THE SYNTHETHIC DATA SET 79
IDENTIFICATION OF A KNOWN AUTOMATON 80
PARAMETER DEPENDENCE 81
STABILITY ANALYSIS 83
QUALITY OF THE ONLINE APPROACH 85
5.2.2 PERFORMANCE OF THE ONLINE APPROACH ON A REAL WORLD
EXAMPLE 86
YEAST GENE EXPRESSION 86
HEPATITIS DATA 86
5.3 CONCLUSION 88
CONTENTS IX
6 AUGEMENTED ITEMSET TREES 91
6.1 RELATED WORK 92
6.2 PROBLEM STATEMENT 93
6.3 PROOF OF MAIN CONCEPT 94
6.4 MAIN IDEA OF THE USED DATA STRUCTURE 96
6.5 DEFINITION OF THE AIST 96
6.5.1 INSERTPATTERN 98
SETNEXTNODE 99
UPDATEMOREGENERALCOUNTS 101
6.5.2 DELETING MFPS 102
6.6 EXPERIMENTS 103
6.6.1 DATA SETS 103
6.6.2 EMPIRICAL EVALUATION 104
6.7 CONCLUSION 110
7 USING CONSTRAINTS ON THE ATTRIBUTE LEVEL FOR PRTA INDUC
TION (CSPRTA) 111
7.1 MAIN IDEA OF ATTRIBUTE LEVEL CONSTRAINTS ILL
7.1.1 PROBLEM SETTING 112
7.2 IMPLEMENTATION 113
7.2.1 IMPLEMENTATION OF MUST-LINK 113
7.2.2 IMPLEMENTATION OF MUST-LINK-EXCLUSIVE 115
7.3 EXPERIMENTS 115
7.3.1 SYNTHETIC CONSTRAINTS 115
7.3.2 YEAST CONSTRAINTS 117
7.3.3 HEPATITIS CONSTRAINTS 120
7.4 CONCLUSION 122
8 ATTRIBUTE CONSTRAINED CLUSTERING 123
8.1 RELATED WORK 124
8.2 CONSTRAINTS ON THE ATTRIBUTE LEVEL 125
8.2.1 PROBLEM DESCRIPTION 125
8.2.2 MUST-LINK 126
8.2.3 MUST-LINK-EXCL 128
8.2.4 CONVERGENCE OF ATTRIBUTE CONSTRAINED CLUSTERING . . . 130
8.2.5 EXTENSION OF ATTRIBUTE CONSTRAINED CLUSTERING .... 130
8.3 EXPERIMENTS 130
8.3.1 DATA SETS 131
8.3.2 EVALUATION OF CONSTRAINED CLUSTERING 132
8.3.3 CONSTRAINT SPECIFICATION COSTS 133
8.3.4 RESULTS MUST-LINK AND MUST-LINK-EXCL 133
RESULTS NUMATTR 133
RESULTS NUMCONSTRAINTS 135
RESULTS NUMFIXEDATTR 135
X
CONTENTS
RESULTS NUMLNSTANCES 139
RESULTS ON THE REAL-WORLD DATA SET 141
8.4 CONCLUSION 143
9 AN ONLINE APPROACH FOR PRTA INDUCTION (OPRTA) 145
9.1 ONLINE INDUCTION OF PRTAS BASED ON MFP CLUSTERING .... 146
9.1.1 PROBLEM SETTING 146
9.1.2 OVERVIEW OF THE APPROACH 147
9.1.3 CREATING THE PRTA ONLINE 149
FUNCTION FNN 150
9.1.4 ADAPTATION FOR AN UNBOUNDED DATA SET WITH CON
CEPT DRIFT 151
THE REPEATED WORLD DATA STREAM SETTING 151
THE CONTINUOUS WORLD DATA STREAM SETTING 153
ALGORITHMIC ADJUSTMENTS 154
9.2 EXPERIMENTS 155
9.2.1 PERFORMANCE ON STREAM SETTING WITHOUT CONCEPT DRIFT 155
STABILITY ANALYSIS 155
PERFORMANCE ON THE REAL WORLD DATA SETS 160
9.2.2 PERFORMANCE ON STREAM SETTING WITH CONCEPT DRIFT . 161
9.3 CONCLUSION 162
10 SUMMARY AND OUTLOOK 165
10.1 SUMMARY 165
10.2 OUTLOOK 167
11 BIBLIOGRAPHY 169
|
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| spelling | Schmidt, Jana A. Verfasser aut Machine learning of timed automata Jana A. Schmidt 2013 X, 179 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier München, Techn. Univ., Diss., 2013 Zeitbehafteter Automat (DE-588)4713349-1 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Maschinelles Lernen (DE-588)4193754-5 s Zeitbehafteter Automat (DE-588)4713349-1 s DE-604 Erscheint auch als Online-Ausgabe urn:nbn:de:bvb:91-diss-20131216-1145664-0-4 http://mediatum.ub.tum.de/node?id=1145664 Verlag kostenfrei Volltext https://nbn-resolving.org/urn:nbn:de:bvb:91-diss-20131216-1145664-0-4 Resolving-System DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027011375&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Schmidt, Jana A. Machine learning of timed automata Zeitbehafteter Automat (DE-588)4713349-1 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4713349-1 (DE-588)4193754-5 (DE-588)4113937-9 |
| title | Machine learning of timed automata |
| title_auth | Machine learning of timed automata |
| title_exact_search | Machine learning of timed automata |
| title_full | Machine learning of timed automata Jana A. Schmidt |
| title_fullStr | Machine learning of timed automata Jana A. Schmidt |
| title_full_unstemmed | Machine learning of timed automata Jana A. Schmidt |
| title_short | Machine learning of timed automata |
| title_sort | machine learning of timed automata |
| topic | Zeitbehafteter Automat (DE-588)4713349-1 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Zeitbehafteter Automat Maschinelles Lernen Hochschulschrift |
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