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Dirichlet enhanced relational learning
2005
Proceedings of the 22nd international conference on Machine learning - ICML '05
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
doi:10.1145/1102351.1102478
dblp:conf/icml/XuTYYK05
fatcat:yj4bffblmfhwdknayez6xscxfy