Relational knowledge discovery:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge Univ. Press
2012
|
| Ausgabe: | 1. publ. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | VI, 271 S. Ill., graph. Darst. |
| ISBN: | 9780521122047 9780521190213 |
Internformat
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Datensatz im Suchindex
| _version_ | 1804149322211655680 |
|---|---|
| adam_text | Contents
About this book Page
1
4
5
8
17
18
32
38
38
46
53
57
67
68
73
74
4
Clustering
76
4.1
Concepts as sets of objects
76
4.2
fc-nearest neighbours
78
4.3
fc-means clustering
81
4.4
Incremental concept formation
85
4.5
Relational clustering
90
5
Information gain
92
5.1
Entropy
93
5.2
Information and information gain
98
5.3
Induction of decision trees
102
5.4
Cain again
109
5.5
Pruning
111
5.6
Conclusion
119
1
Introduction
1.1
Motivation
1.2
Related disciplines
2
Relational knowledge
2.1
Objects and their attributes
2.2
Knowledge structures
3
From data to hypotheses
3.1
Representation
3.2
Changing the representation
3.3
Samples
3.4
Evaluation of hypotheses
3.5
Learning
3.6
Bias
3.7
Overfitting
3.8
Summary
vi
Contents
6
Rough set theory
121
6.1
Knowledge and discernability
121
6.2
Rough knowledge
127
6.3
Rough knowledge structures
137
6.4
Relative knowledge
141
6.5
Knowledge discovery
149
6.6
Conclusion
156
7
Inductive logic learning
159
7.1
From information systems to logic programs
160
7.2
Horn logic
167
7.3
Heuristic rule induction
180
7.4
Inducing Horn theories from data
189
7.5
Summary
221
8
Learning and ensemble learning
224
8.1
Learnability
224
8.2
Decomposing the learning problem
234
8.3
Improving by focusing on errors
239
8.4
A relational view on ensemble learning
244
8.5
Summary
249
9
The logic of knowledge
251
9.1
Knowledge representation
251
9.2
Learning
253
9.3
Summary
256
Notation
258
References
261
Index
267
What is knowledge and how is it represented? This book focuses on
the idea of formalising knowledge as relations, interpreting knowledge
represented in databases or logic programs as relational data, and
discovering new knowledge by identifying hidden and defining new
relations. After a brief introduction to representational issues, the
author develops a relational language for abstract machine learning
problems. He then uses this language to discuss traditional methods
such as clustering and decision tree induction, before moving on to
two previously underestimated topics that are again coming to the
fore: rough set data analysis and inductive logic programming.
Its clear and precise presentation is ideal for undergraduate computer
science students. The book will also interest those who study artificial
intelligence or machine learning at the graduate level. Exercises are
provided and each concept is introduced using the same example
domain, making it easier to compare the individual properties of
different approaches.
M. E.
Müller
is a Professor of Computer Science at the University of
olied Sciences, Bonn-Rhein-Sieg.
|
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| dewey-full | 006.3/1 |
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| discipline | Informatik |
| edition | 1. publ. |
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| id | DE-604.BV040303757 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:21:11Z |
| institution | BVB |
| isbn | 9780521122047 9780521190213 |
| language | English |
| lccn | 2011049968 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025158724 |
| oclc_num | 802615050 |
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| physical | VI, 271 S. Ill., graph. Darst. |
| publishDate | 2012 |
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| spelling | Müller, Martin E. 1970- Verfasser (DE-588)1026449359 aut Relational knowledge discovery M. E. Müller 1. publ. Cambridge Cambridge Univ. Press 2012 VI, 271 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Computational learning theory Machine learning Relational databases COMPUTERS / Database Management / General bisacsh Datenbankverwaltung (DE-588)4389357-0 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Wissensrepräsentation (DE-588)4049534-6 gnd rswk-swf Wissensrepräsentation (DE-588)4049534-6 s Datenbankverwaltung (DE-588)4389357-0 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025158724&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025158724&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Müller, Martin E. 1970- Relational knowledge discovery Computational learning theory Machine learning Relational databases COMPUTERS / Database Management / General bisacsh Datenbankverwaltung (DE-588)4389357-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Wissensrepräsentation (DE-588)4049534-6 gnd |
| subject_GND | (DE-588)4389357-0 (DE-588)4193754-5 (DE-588)4049534-6 |
| title | Relational knowledge discovery |
| title_auth | Relational knowledge discovery |
| title_exact_search | Relational knowledge discovery |
| title_full | Relational knowledge discovery M. E. Müller |
| title_fullStr | Relational knowledge discovery M. E. Müller |
| title_full_unstemmed | Relational knowledge discovery M. E. Müller |
| title_short | Relational knowledge discovery |
| title_sort | relational knowledge discovery |
| topic | Computational learning theory Machine learning Relational databases COMPUTERS / Database Management / General bisacsh Datenbankverwaltung (DE-588)4389357-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Wissensrepräsentation (DE-588)4049534-6 gnd |
| topic_facet | Computational learning theory Machine learning Relational databases COMPUTERS / Database Management / General Datenbankverwaltung Maschinelles Lernen Wissensrepräsentation |
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