Relational Sequence Learning

Kristian Kersting; Luc De Raedt; Bernd Gutmann; Andreas Karwath; Niels Landwehr

doi:10.1007/978-3-540-78652-8_2

Relational Sequence Learning

Kristian Kersting, Luc De Raedt, Bernd Gutmann, Andreas Karwath, Niels Landwehr

Research output: Chapter in Book/Report/Conference proceeding › Chapter

10 Citations (Scopus)

Abstract

Sequential behavior and sequence learning are essential to intelligence. Often the elements of sequences exhibit an internal structure that can elegantly be represented using relational atoms. Applying traditional sequential learning techniques to such relational sequences requires one either to ignore the internal structure or to live with a combinatorial explosion of the model complexity. This chapter briefly reviews relational sequence learning and describes several techniques tailored towards realizing this, such as local pattern mining techniques, (hidden) Markov models, conditional random fields, dynamic programming and reinforcement learning.

Original language	English
Title of host publication	Probabilistic Inductive Logic Programming - Theory and Applications
Place of Publication	Berlin Heidelberg, Germany
Publisher	Springer Verlag
Pages	28-55
Number of pages	28
Volume	4911
ISBN (Print)	978-3-540-78651-1
DOIs	https://doi.org/10.1007/978-3-540-78652-8_2
Publication status	Published - 2008

Keywords

inductive logic programming, machine learning, relational learning, scientific knowledge

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10.1007/978-3-540-78652-8_2

http://dx.doi.org/10.1007/978-3-540-78652-8_2

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AU - Karwath, Andreas

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Relational Sequence Learning

Abstract

Keywords

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