Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Using relational knowledge discovery to prevent securities fraud
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Finding tribes: identifying close-knit individuals from employment patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Relational data pre-processing techniques for improved securities fraud detection
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Automatic identification of quasi-experimental designs for discovering causal knowledge
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A rough set approach to mining connections from information systems
Proceedings of the 2010 ACM Symposium on Applied Computing
A rough set approach to multiple dataset analysis
Applied Soft Computing
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Research over the past several decades in learning logical and probabilistic models has greatly increased the range of phenomena that machine learning can address. Recent work has extended these boundaries even further by unifying these two powerful learning frameworks. However, new frontiers await. Current techniques are capable of learning only a subset of the knowledge needed by practitioners in important domains, and further unification of probabilistic and logical learning offers a unique ability to produce the full range of knowledge needed in a wide range of applications.