A survey on statistical relational learning

Khosravi, Hassan and Bina, Bahareh (2010). A survey on statistical relational learning. In: Advances in Artificial Intelligence - 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Proceedings. 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, ON, Canada, (256-268). 31 May - 2 June 2010. doi:10.1007/978-3-642-13059-5_25


Author Khosravi, Hassan
Bina, Bahareh
Title of paper A survey on statistical relational learning
Conference name 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010
Conference location Ottawa, ON, Canada
Conference dates 31 May - 2 June 2010
Proceedings title Advances in Artificial Intelligence - 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Proceedings   Check publisher's open access policy
Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Place of Publication Berlin, Germany
Publisher Springer
Publication Year 2010
Sub-type Fully published paper
DOI 10.1007/978-3-642-13059-5_25
Open Access Status Not yet assessed
ISBN 3642130585
ISSN 0302-9743
Volume 6085 LNAI
Start page 256
End page 268
Total pages 13
Abstract/Summary The vast majority of work in Machine Learning has focused on propositional data which is assumed to be identically and independently distributed, however, many real world datasets are relational and most real world applications are characterized by the presence of uncertainty and complex relational structure where the data distribution is neither identical nor independent. An emerging research area, Statistical Relational Learning(SRL), attempts to represent, model, and learn in relational domain. Currently, SRL is still at a primitive stage in Canada, which motivates us to conduct this survey as an attempt to raise more attention to this field. Our survey presents a brief introduction to SRL and a comparison with conventional learning approaches. In this survey we review four SRL models(PRMs, MLNs, RDNs, and BLPs) and compare them theoretically with respect to their representation, structure learning, parameter learning, and inference methods. We conclude with a discussion on limitations of current methods.
Subjects 1700 Computer Science
2614 Theoretical Computer Science
Q-Index Code E1
Institutional Status Non-UQ

Document type: Conference Paper
Collection: Institute for Teaching and Learning Innovation Publications
 
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Created: Thu, 15 Sep 2016, 02:15:42 EST by Hassan Khosravi on behalf of Learning and Research Services (UQ Library)