Communications of the ACM
First-order jk-clausal theories are PAC-learnable
Artificial Intelligence
Machine Learning - special issue on inductive logic programming
Confirmation-guided discovery of first-order rules with tertius
Machine Learning
Machine Learning
Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
ACM SIGMOD Record
Data Streams: Models and Algorithms (Advances in Database Systems)
Data Streams: Models and Algorithms (Advances in Database Systems)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Learning Horn Expressions with LOGAN-H
The Journal of Machine Learning Research
Mining Predictive k-CNF Expressions
IEEE Transactions on Knowledge and Data Engineering
Regression on evolving multi-relational data streams
Proceedings of the 2011 Joint EDBT/ICDT Ph.D. Workshop
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With the increasing popularity of data streams it has become time to adapt logical and relational learning techniques for dealing with streams. In this note, we present our preliminary results on upgrading the clausal discovery paradigm towards the mining of streams. In this setting, there is a stream of interpretations and the goal is to learn a clausal theory that is satisfied by these interpretations. Furthermore, in data streams the interpretations can be read (and processed) only once.