The Strength of Weak Learnability
Machine Learning
PAC-learnability of determinate logic programs
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Horn approximations of empirical data
Artificial Intelligence
Theoretical Computer Science
A Machine-Oriented Logic Based on the Resolution Principle
Journal of the ACM (JACM)
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Machine Learning
Reasoning with Incomplete Information: Rough Set Based Information Logics
Proceedings of the SOFTEKS Workshop on Incompleteness and Uncertainty in Information Systems
Function-Free Horn Clauses Are Hard to Approximate
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
A new rough sets model based on database systems
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Relational attribute systems II: reasoning with relations in information structures
Transactions on rough sets VII
On generalizing rough set theory
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Rough set approximations in formal concept analysis and knowledge spaces
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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While the popularity of statistical, probabilistic and exhaustive machine learning techniques still increases, relational and logic approaches are still a niche market in research. While the former approaches focus on predictive accuracy, the latter ones prove to be indispensable in knowledge discovery. In this paper we present a relational description of machine learning problems. We demonstrate how common ensemble learning methods as used in classifier learning can be reformulated in a relational setting. It is shown that multimodal logics and relational data analysis with rough sets are closely related. Finally, we give an interpretation of logic programs as approximations of hypotheses. It is demonstrated that at a certain level of abstraction all these methods unify into one and the same formalisation which nicely connects to multimodal operators.