<div class="csl-bib-body">
<div class="csl-entry">Oosterhuis, J. (2020). <i>Weight learning in LP MLN for collective classification</i> [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2020.66402</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2020.66402
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/1215
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dc.description.abstract
In this thesis we investigate in detail the theory and practice of weight learning in LP MLN for collective classification problems. We test different weight learning methods on a practical collective classification problem, namely object labeling in images using relational context constraints, based on an exposition of the theory of weight learning in LP MLN. In our experiments we consider the applicability of different systems for weight learning in LP MLN and evaluate the performance of different weight learning methods in different scenarios. As we show, the best learning method depends very much on the input program and the specific dataset, and no single method noticeably outperforms other methods in our tests, with very simple learning methods performing very well. The performance of different methods can be very hard to predict, as some of our results are very unexpected. Nonetheless, from our experiments we conclude that weight learning noticeably improves the performance of an LP MLN-program in a collective classification setup and that effective weight learning methods and systems exist for LP MLN .
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
Knowledge Representation
de
dc.subject
Answer Set Programming
de
dc.subject
statistical relational learning
de
dc.subject
Markov Logic
de
dc.subject
collective classification
de
dc.subject
weight learning
de
dc.subject
parameter learning
de
dc.subject
LPMLN
de
dc.subject
Knowledge Representation
en
dc.subject
Answer Set Programming
en
dc.subject
statistical relational learning
en
dc.subject
Markov Logic
en
dc.subject
collective classification
en
dc.subject
weight learning
en
dc.subject
parameter learning
en
dc.subject
LPMLN
en
dc.title
Weight learning in LP MLN for collective classification
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dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2020.66402
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Jacco Oosterhuis
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dc.publisher.place
Wien
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tuw.version
vor
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tuw.thesisinformation
Technische Universität Wien
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dc.contributor.assistant
Kaminski, Tobias Dietmar
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tuw.publication.orgunit
E192 - Institut für Logic and Computation
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dc.type.qualificationlevel
Diploma
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dc.identifier.libraryid
AC15604381
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dc.description.numberOfPages
89
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dc.identifier.urn
urn:nbn:at:at-ubtuw:1-135161
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dc.thesistype
Diplomarbeit
de
dc.thesistype
Diploma Thesis
en
dc.rights.identifier
In Copyright
en
dc.rights.identifier
Urheberrechtsschutz
de
tuw.advisor.staffStatus
staff
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tuw.assistant.staffStatus
staff
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tuw.advisor.orcid
0000-0001-6003-6345
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item.fulltext
with Fulltext
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item.cerifentitytype
Publications
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item.mimetype
application/pdf
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item.openairecristype
http://purl.org/coar/resource_type/c_bdcc
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item.languageiso639-1
en
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item.openaccessfulltext
Open Access
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item.openairetype
master thesis
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item.grantfulltext
open
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crisitem.author.dept
E105 - Institut für Stochastik und Wirtschaftsmathematik