Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Propositional circumscription and extended closed-world reasoning are &Pgr;p2-complete
Theoretical Computer Science
Machine Learning - special issue on inductive logic programming
A tutorial on learning with Bayesian networks
Learning in graphical models
Efficient identification of Web communities
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Proceedings of the 1st ACM SIGACT-SIGMOD symposium on Principles of database systems
PODS '82 Proceedings of the 1st ACM SIGACT-SIGMOD symposium on Principles of database systems
Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Link Based Clustering of Web Search Results
WAIM '01 Proceedings of the Second International Conference on Advances in Web-Age Information Management
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Why collective inference improves relational classification
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Correctness of Local Probability Propagation in Graphical Models with Loops
Neural Computation
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Contextual word spotting in historical manuscripts using Markov logic networks
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
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The vast majority of work in Machine Learning has focused on propositional data which is assumed to be identically and independently distributed, however, many real world datasets are relational and most real world applications are characterized by the presence of uncertainty and complex relational structure where the data distribution is neither identical nor independent An emerging research area, Statistical Relational Learning(SRL), attempts to represent, model, and learn in relational domain Currently, SRL is still at a primitive stage in Canada, which motivates us to conduct this survey as an attempt to raise more attention to this field Our survey presents a brief introduction to SRL and a comparison with conventional learning approaches In this survey we review four SRL models(PRMs, MLNs, RDNs, and BLPs) and compare them theoretically with respect to their representation, structure learning, parameter learning, and inference methods We conclude with a discussion on limitations of current methods.