Relational sequence learning

Authors:
Kristian Kersting;Luc De Raedt;Bernd Gutmann;Andreas Karwath;Niels Landwehr
Affiliations:
CSAIL, Massachusetts Institute of Technology, Cambridge, MA;Departement Computerwetenschappen, K.U. Leuven, Heverlee, Belgium;Departement Computerwetenschappen, K.U. Leuven, Heverlee, Belgium;Machine Learning Lab, Institute for Computer Science, University of Freiburg, Freiburg, Germany;Machine Learning Lab, Institute for Computer Science, University of Freiburg, Freiburg, Germany
Venue:
Probabilistic inductive logic programming
Year:
2008

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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.