Relational Data Mining
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Integrating Naïve Bayes and FOIL
The Journal of Machine Learning Research
Modeling interleaved hidden processes
Proceedings of the 25th international conference on Machine learning
A Simple Model for Sequences of Relational State Descriptions
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
kFOIL: learning simple relational kernels
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Statistical Relational Learning (SRL) is concerned with building statistical models for relational data. While SRL approaches have shown much potential in complex real-world application domains, their computational complexity remains a major issue and often limits their practical applicability. This thesis is concerned with relatively simple yet efficient SRL techniques. We show how expressivity and generality can be traded for efficiency by restricting model complexity and developing special-purpose inference and learning algorithms that take advantage of such restrictions, as well as by tailoring models to specific application domains.