Statistical relational learning: Difference between revisions

Revision as of 16:47, 10 August 2015

Revision as of 15:53, 7 September 2015

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== Canonical tasks ==

A number of canonical tasks are associated with statistical relational learning, the most common ones being<ref>Matthew Richardson and Pedro Domingos, [http://www.cs.washington.edu/homes/pedrod/papers/mlj05.pdf "Markov Logic Networks.]" ''Machine Learning'', '''62''' (2006), pp. 107–136.</ref>

A number of canonical tasks are associated with statistical relational learning, the most common ones being<ref>Matthew Richardson and Pedro Domingos, [http://www.cs.washington.edu/homes/pedrod/papers/mlj05.pdf "Markov Logic Networks.]" ''Machine Learning'', '''62''' (2006), pp 107–136.</ref>

* [[collective classification]], i.e. the (simultaneous) [[classification (machine learning)|prediction of the class]] of several objects given objects' attributes and their relations

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== Representation formalisms ==

{{More footnotes|date=June 2011}}

One of the fundamental design goals of the representation formalisms developed in SRL is to abstract away from concrete entities and to represent instead general principles that are intended to be universally applicable. Since there are countless ways in which such principles can be represented, many representation formalisms have been proposed in recent years.<ref>Lise Getoor and Ben Taskar: Introduction to statistical relational learning, MIT Press, 2007</ref> In the following, some of the more common ones are listed in alphabetical order:

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* [[Markov logic network]]s

* [[Multi-entity Bayesian network]]

* Probabilistic relational model -– a Probabilistic relationalRelational modelModel (PRM) is the counterpart of a [[Bayesian network]] in statistical relational learning.<ref>Friedman N, Getoor L, Koller D, Pfeffer A. (1999) [http://ijcai.org/Past%20Proceedings/IJCAI-99%20VOL-2/PDF/090.pdf "Learning probabilistic relational models"]. In: ''International joint conferences on artificial intelligence'', 1300–09</ref><ref>Teodor Sommestad, Mathias Ekstedt, Pontus Johnson (2010) "A probabilistic relational model for security risk analysis", ''Computers & Security'', 29 (6), 659-679 {{DOI|10.1016/j.cose.2010.02.002}}</ref>

* [[Probabilistic soft logic]]

* [[Recursive random field]]

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* [[Relational Markov network]]

* [[Relational Kalman filtering]]

{{More footnotes|date=June 2011}}

== See also ==

* [[Association rule learning]]

* [[Formal concept analysis]]

* [[Fuzzy logic]]

* [[Grammar induction]]

== Resources ==

* [[Lise Getoor]] and [[Ben Taskar]]: ''Introduction to statistical relational learning'', MIT Press, 2007

* Brian Milch, and Stuart J. Russell: ''First-Order Probabilistic Languages: Into the Unknown'', Inductive Logic Programming, volume 4455 of [[Lecture Notes in Computer Science]], page 10–24. Springer, 2006

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== References ==

{{reflist}}