Learning and modeling with probabilistic conditional logic:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Heidelberg
Akad. Verl.-Ges. AKA [u.a.]
2010
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| Schriftenreihe: | Dissertationen zur künstlichen Intelligenz
328 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XI, 223 S. graph. Darst. |
| ISBN: | 9781607500988 9783898383288 |
Internformat
MARC
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Datensatz im Suchindex
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| adam_text | CONTENTS 1 INTRODUCTION 1 1.1 LEARNING WITH PROBABILISTIC CONDITIONAL
LOGIC 3 1.2 LEARNING FOR MODELING 4 1.3 MODELING WITH FIRST-ORDER
PROBABILISTIC CONDITIONAL LOGIC . . . 5 1.4 OVERVIEW 7 2 PROPOSITIONAL
GRAPHICAL MODELS 9 2.1 PROBABILITY THEORY 9 2.2 GRAPHICAL MODELS 15
2.2.1 UNDIRECTED GRAPHICAL MODELS 17 2.2.2 DIRECTED GRAPHICAL MODELS 26
2.2.3 OTHER GRAPHICAL MODELS 30 2.3 USING CONSTRAINTS FOR SPECIFYING
GRAPHICAL MODELS 31 2.3.1 PROBABILISTIC CONSTRAINTS 31 2.3.2
REPRESENTATIONAL ASPECTS OF JOINT PROBABILITY FUNCTIONS WITH MAXIMUM
ENTROPY 39 2.4 PROPOSITIONAL PROBABILISTIC CONDITIONAL LOGIC 41 2.4.1
PROPOSITIONAL LOGIC 42 2.4.2 PROBABILISTIC CONDITIONAL LOGIC 43 2.5
LEARNING GRAPHICAL MODELS FROM DATA 46 2.5.1 GENERAL ASPECTS 47 2.5.2
LEARNING UNDIRECTED GRAPHICAL MODELS 52 2.6 CONCLUSIONS 56 3 LEARNING
PROBABILISTIC CONDITIONALS FROM DATA 57 3.1 INTRODUCTION 58 3.2
REVERSING INDUCTIVE KNOWLEDGE REPRESENTATION 58 3.3 USING HASKELL FOR
DATA MINING 67 3.4 ENUMERATING ALL CYCLES OF AN UNDIRECTED GRAPH 69
3.4.1 CYCLES IN AN UNDIRECTED GRAPH 69 3.4.2 A COMBINED APPROACH 71 IX
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/99967692X DIGITALISIERT
DURCH 6.6 CONCLUSIONS 146 3.5 A GRAPHICAL USER INTERFACE FOR KNOWLEDGE
DISCOVERY 74 3.6 LESSONS LEARNED 78 3.6.1 CLEAN AND CONCISE SYNTAX 78
3.6.2 STRONG TYPING 79 3.6.3 A COMPREHENSIVE STANDARD LIBRARY 82 3.6.4 A
SUITABLE DEVELOPMENT ENVIRONMENT 83 3.6.5 BATTLING WITH LAZINESS AND
FIGHTING EXCESSIVE MEMORY CONSUMPTION 85 3.7 CONCLUSIONS 86 4 DATA
FUSION WITH PROBABILISTIC CONDITIONAL LOGIC 87 4.1 INTRODUCTION TO DATA
FUSION 87 4.2 DATA FUSION WITH CONDORCKD AND SPIRIT 90 4.3 EXPERIMENTS
93 4.3.1 FUSING SYNTHETIC DATA SETS 93 4.3.2 FUSING INCONSISTENT DATA
100 4.3.3 FUSING REAL-WORLD DATA 107 4.4 CONCLUSIONS 110 5 RELATIONAL
PROBABILISTIC MODELS 111 5.1 INTRODUCTION ILL 5.2 KNOWLEDGE-BASED MODEL
CONSTRUCTION 113 5.3 PROBABILISTIC RELATIONAL MODELS 113 5.4 BAYESIAN
LOGIC PROGRAMS 114 5.5 MARKOV LOGIC NETWORKS 116 5.6 CONCLUSIONS 117 6
FIRST-ORDER PROBABILISTIC CONDITIONAL LOGIC 119 6.1 MOTIVATION FOR
FIRST-ORDER PROBABILISTIC CONDITIONAL LOGIC . . . 120 6.2 SYNTAX OF
FIRST-ORDER PROBABILISTIC CONDITIONAL LOGIC 124 6.3 SEMANTICS OF
FIRST-ORDER PROBABILISTIC CONDITIONAL LOGIC . . .132 6.3.1 LOGICAL
SEMANTICS 133 6.3.2 PROBABILISTIC SEMANTICS 135 6.4 MAXIMUM
ENTROPY-BASED MODEL SELECTION FOR FIRST-ORDER PROBA- BILISTIC
CONDITIONAL LOGIC 138 6.5 EXAMPLES 140 7 REPRESENTING MAXIMUM-ENTROPY
MODELS FOR FIRST-ORDER PROBABI- LISTIC CONDITIONAL LOGIC 147 7.1
MAXIMUM-ENTROPY MODELS WITH PARAMETER SHARING 148 7.1.1 PARAMETRIC
EQUIVALENCE AND PARAMETER SHARING . . . .149 7.1.2 PARAMETRIC UNIFORMITY
IMPLIES PARAMETER SHARING . . .151 7.2 A SYNTACTIC CRITERION FOR
PARAMETRIC EQUIVALENCE 153 7.2.1 PROBABILISTIC CONSTRAINT INVOLUTIONS
154 7.2.2 EXAMPLES 157 7.3 PROPERTIES OF PROBABILISTIC CONSTRAINT
INVOLUTIONS 163 7.3.1 PROBABILISTIC CONSTRAINTS AS A SYSTEM OF LINEAR
EQUATIONS! 64 7.3.2 APPLYING ***(*) AS A COLUMN PERMUTATION 165 7.3.3
APPLYING TTJ( K ) AS A ROW PERMUTATION 166 7.3.4 PROBABILISTIC
CONSTRAINT INVOLUTIONS AND PARAMETRIC EQUIVALENCE 168 7.4 A SYNTACTIC
CRITERION FOR PARAMETER SHARING 1 70 7.4.1 INVOLUTION COVERING 173 7.4.2
INVOLUTION COVERING IMPLIES PARAMETER SHARING . . . . 1 74 7.4.3
EXAMPLES FOR INVOLUTION COVERINGS 176 7.5 TRANSFORMING FO-PCL KNOWLEDGE
BASES FOR PARAMETER SHARING. 1 78 7.5.1 PARAMETRIC NON-UNIFORMITY CAUSED
BY INTER-RULE INTERAC- TIONS 179 7.5.2 PARAMETRIC NON-UNIFORMITY CAUSED
BY INTRA-RULE INTERAC- TIONS 183 7.5.3 TOWARDS AN ALGORITHM FOR ENSURING
PARAMETER SHARING . 185 7.6 FO-PCL KNOWLEDGE BASES WITH REDUNDANT
CONDITIONALS . . . .186 7.7 CONCLUSIONS 189 8 CONCLUSIONS AND FURTHER
WORK 191 A
|
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| indexdate | 2024-07-09T22:08:58Z |
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| isbn | 9781607500988 9783898383288 |
| language | English |
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| physical | XI, 223 S. graph. Darst. |
| publishDate | 2010 |
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| series | Dissertationen zur künstlichen Intelligenz |
| series2 | Dissertationen zur künstlichen Intelligenz |
| spelling | Fisseler, Jens Verfasser aut Learning and modeling with probabilistic conditional logic Jens Fisseler Heidelberg Akad. Verl.-Ges. AKA [u.a.] 2010 XI, 223 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Dissertationen zur künstlichen Intelligenz 328 Zugl.: Hagen, FernUniv., Diss., 2009 Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Logik (DE-588)4036202-4 gnd rswk-swf Wahrscheinlichkeit (DE-588)4137007-7 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Logik (DE-588)4036202-4 s Wahrscheinlichkeit (DE-588)4137007-7 s Künstliche Intelligenz (DE-588)4033447-8 s DE-604 Dissertationen zur künstlichen Intelligenz 328 (DE-604)BV005345280 328 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018874729&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Fisseler, Jens Learning and modeling with probabilistic conditional logic Dissertationen zur künstlichen Intelligenz Künstliche Intelligenz (DE-588)4033447-8 gnd Logik (DE-588)4036202-4 gnd Wahrscheinlichkeit (DE-588)4137007-7 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4036202-4 (DE-588)4137007-7 (DE-588)4113937-9 |
| title | Learning and modeling with probabilistic conditional logic |
| title_auth | Learning and modeling with probabilistic conditional logic |
| title_exact_search | Learning and modeling with probabilistic conditional logic |
| title_full | Learning and modeling with probabilistic conditional logic Jens Fisseler |
| title_fullStr | Learning and modeling with probabilistic conditional logic Jens Fisseler |
| title_full_unstemmed | Learning and modeling with probabilistic conditional logic Jens Fisseler |
| title_short | Learning and modeling with probabilistic conditional logic |
| title_sort | learning and modeling with probabilistic conditional logic |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd Logik (DE-588)4036202-4 gnd Wahrscheinlichkeit (DE-588)4137007-7 gnd |
| topic_facet | Künstliche Intelligenz Logik Wahrscheinlichkeit Hochschulschrift |
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