Active relational rule learning in a constrained confidence-rated boosting framework:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Marburg
Tectum
2005
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Zugl.: Bonn, Univ., Diss., 2004 |
| Beschreibung: | XIV, 234 S. graph. Darst. 210 mm x 148 mm |
| ISBN: | 3828888364 |
Internformat
MARC
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| 100 | 1 | |a Hoche, Susanne |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Active relational rule learning in a constrained confidence-rated boosting framework |c Susanne Hoche |
| 264 | 1 | |a Marburg |b Tectum |c 2005 | |
| 300 | |a XIV, 234 S. |b graph. Darst. |c 210 mm x 148 mm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Zugl.: Bonn, Univ., Diss., 2004 | ||
| 650 | 0 | 7 | |a Relationales Datenmodell |0 (DE-588)4418263-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Boosting |0 (DE-588)4839853-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Regellernen |0 (DE-588)4389503-7 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Induktive logische Programmierung |0 (DE-588)4807625-9 |2 gnd |9 rswk-swf |
| 655 | 7 | |0 (DE-588)4113937-9 |a Hochschulschrift |2 gnd-content | |
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| 856 | 4 | 2 | |m Digitalisierung UB Regensburg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=013331327&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-013331327 | ||
Datensatz im Suchindex
| _version_ | 1804133565405855744 |
|---|---|
| adam_text | Table
of Contents
Chapter
1.
Introduction
..................... 1
1.1
Learning from Examples
.............. 1
1.2
Representation Formalisms for Learning from Ex¬
amples
........................ 3
1.3
Relational Rule Learning in a Boosting Framework
5
1.4
Active Relational Feature Selection
........ 8
1.5
Objectives and Contributions of the Dissertation
. 9
1.6
Organisation of the Dissertation
.......... 13
1.7
Publications
..................... 15
Chapter
2.
Fundamentals of Relational Rule Learning
.... IG
2.1
Concept Learning from Examples
......... IG
2.2
Representation Formalisms
............. 21
2.3
Inductive Logic Programming
........... 30
2.3.1
Fundamentals of Logic Programming
... 30
2.3.2
Representation of Examples and Hypotheses
33
2.3.3
Automated Generation of Hypotheses
. . . 43
2.4
Summary
...................... 49
Chapter
3.
Boosting Relational Rule Learners
......... 51
3.1
Boosting
....................... 52
3.2
Existing Approaches to Boosting in Relational Learn¬
ing
.......................... 57
3.3
Constrained Confidence-Rated ILP Boosting
...
GO
3.3.1
Constraining the Hypotheses in C2RIB
. .
GO
3.3.2
Understandability
oïC-RIB s
Combined Hy¬
pothesis
................... 65
3.3.3
The C2RIB Algorithm
............ 67
3.3.4
Optimisation Criteria
............ 71
3.3.4.1
Definition of a Base Hypothesis
Prediction Confidence
...... 72
3.3.4.2
Modification of the Examples Dis¬
tribution D
............ 81
3.4
The Relational Rule Learner RRuLe
........ 84
3.4.1
The Search Strategy of RRuLe
....... 86
3.4.2
The Myopia Problem of Greedy Learners
. 90
3.4.3
The Refinement Operator of RRuLe
.... 92
3.4.4
The Refinement of Continuous Variables in
RRuLe
.................... 94
3.5
Empirical Evaluation
................ 96
3.5.1
Experiments on ILP Benchmark Domains
. 97
3.5.1.1
Data Sets
............. 98
3.5.1.2
Empirical Results
......... 106
3.5.2
Experiments on General Knowledge and Data
Mining Tasks
................ 113
3.5.2.1
Data Sets
............. 113
3.5.2.2
Empirical Results
......... 116
3.5.3
Experiments on Prepositional Domains
. . 119
3.6
Related Work
.................... 120
3.7
Summary
...................... 124
Chapter
4.
Active Feature Selection in a Boosted ILP Learner
126
4.1
Feature Selection
.................. 127
4.2
Active Feature Selection in C2RIB
......... 130
4.2.1
Features in the Framework of C2RIB
... 131
4.2.2
Determining the Initial Feature Order in
CPRIB0
.................... 132
4.2.3
Inclusion of Features and Relations in C^RIB0
136
4.2.4
Monitoring the Learning Progress in C2RIBD
139
4.3
Empirical Evaluation
................ 148
4.3.1
Experiments on Feature Subset Size and
Learning Time
................ 151
4.3.2
Experiments on Passive versus Active Fea¬
ture Selection
................ 155
4.3.2.1
Experiments on an Artificial Do¬
main
................ 156
4.3.2.2
Experiments on Real World Do¬
mains
............... 159
4.4
Related Work
.................... 171
4.5
Summary
...................... 173
Chapter
5.
Active Feature Selection in C2RIB Revisited
... 175
5.1
More Informed Approaches to Feature Selection
. 176
5.1.1
Determining the Current Feature Order
. . 178
5.1.2
Determining the Current Feature Subset
. 185
5.2
Empirical Evaluation
................
18G
5.2.1
Experimental Design
............
18G
5.2.2
Detailed Results and Discussion
...... 190
5.2.3
Summary and Implications
......... 195
5.2.4
A Concluding Comparison
......... 196
5.3
Active Feature Selection in a Relation-Oriented Set¬
ting
.......................... 199
5.4
Summary
...................... 203
Chapter
6.
Conclusions and Future Work
........... 205
6.1
Contributions of this Dissertation
.........
20G
6.2
Future Research Directions
............. 209
Table of Symbols
......................... 214
Index
............................... 217
References
............................. 223
|
| any_adam_object | 1 |
| author | Hoche, Susanne |
| author_facet | Hoche, Susanne |
| author_role | aut |
| author_sort | Hoche, Susanne |
| author_variant | s h sh |
| building | Verbundindex |
| bvnumber | BV020009752 |
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| ctrlnum | (OCoLC)76759490 (DE-599)BVBBV020009752 |
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| dewey-ones | 004 - Computer science |
| dewey-raw | 004 |
| dewey-search | 004 |
| dewey-sort | 14 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
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| indexdate | 2024-07-09T20:10:44Z |
| institution | BVB |
| isbn | 3828888364 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-013331327 |
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| physical | XIV, 234 S. graph. Darst. 210 mm x 148 mm |
| publishDate | 2005 |
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| publisher | Tectum |
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| spelling | Hoche, Susanne Verfasser aut Active relational rule learning in a constrained confidence-rated boosting framework Susanne Hoche Marburg Tectum 2005 XIV, 234 S. graph. Darst. 210 mm x 148 mm txt rdacontent n rdamedia nc rdacarrier Zugl.: Bonn, Univ., Diss., 2004 Relationales Datenmodell (DE-588)4418263-6 gnd rswk-swf Boosting (DE-588)4839853-6 gnd rswk-swf Regellernen (DE-588)4389503-7 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Induktive logische Programmierung (DE-588)4807625-9 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Maschinelles Lernen (DE-588)4193754-5 s Regellernen (DE-588)4389503-7 s Relationales Datenmodell (DE-588)4418263-6 s Boosting (DE-588)4839853-6 s Induktive logische Programmierung (DE-588)4807625-9 s DE-604 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=013331327&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hoche, Susanne Active relational rule learning in a constrained confidence-rated boosting framework Relationales Datenmodell (DE-588)4418263-6 gnd Boosting (DE-588)4839853-6 gnd Regellernen (DE-588)4389503-7 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Induktive logische Programmierung (DE-588)4807625-9 gnd |
| subject_GND | (DE-588)4418263-6 (DE-588)4839853-6 (DE-588)4389503-7 (DE-588)4193754-5 (DE-588)4807625-9 (DE-588)4113937-9 |
| title | Active relational rule learning in a constrained confidence-rated boosting framework |
| title_auth | Active relational rule learning in a constrained confidence-rated boosting framework |
| title_exact_search | Active relational rule learning in a constrained confidence-rated boosting framework |
| title_full | Active relational rule learning in a constrained confidence-rated boosting framework Susanne Hoche |
| title_fullStr | Active relational rule learning in a constrained confidence-rated boosting framework Susanne Hoche |
| title_full_unstemmed | Active relational rule learning in a constrained confidence-rated boosting framework Susanne Hoche |
| title_short | Active relational rule learning in a constrained confidence-rated boosting framework |
| title_sort | active relational rule learning in a constrained confidence rated boosting framework |
| topic | Relationales Datenmodell (DE-588)4418263-6 gnd Boosting (DE-588)4839853-6 gnd Regellernen (DE-588)4389503-7 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Induktive logische Programmierung (DE-588)4807625-9 gnd |
| topic_facet | Relationales Datenmodell Boosting Regellernen Maschinelles Lernen Induktive logische Programmierung Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=013331327&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT hochesusanne activerelationalrulelearninginaconstrainedconfidenceratedboostingframework |