A first course in database systems
A first course in database systems
Statistical methods for speech recognition
Statistical methods for speech recognition
Relational Data Mining
Inductive logic programming for knowedge discovery in databases
Relational Data Mining
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Learning probabilistic models of link structure
The Journal of Machine Learning Research
ACM SIGKDD Explorations Newsletter
A nonparametric hierarchical bayesian framework for information filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Switching and Learning in Feedback Systems
Unsupervised prediction of citation influences
Proceedings of the 24th international conference on Machine learning
Statistical predicate invention
Proceedings of the 24th international conference on Machine learning
Topic-link LDA: joint models of topic and author community
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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We apply nonparametric hierarchical Bayesian modelling to relational learning. In a hierarchical Bayesian approach, model parameters can be "personalized", i.e., owned by entities or relationships, and are coupled via a common prior distribution. Flexibility is added in a nonparametric hierarchical Bayesian approach, such that the learned knowledge can be truthfully represented. We apply our approach to a medical domain where we form a nonparametric hierarchical Bayesian model for relations involving hospitals, patients, procedures and diagnosis. The experiments show that the additional flexibility in a nonparametric hierarchical Bayes approach results in a more accurate model of the dependencies between procedures and diagnosis and gives significantly improved estimates of the probabilities of future procedures.