Dirichlet enhanced relational learning

Zhao Xu, Volker Tresp, Kai Yu, Shipeng Yu, Hans-Peter Kriegel
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
more » ... lving 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.
doi:10.1145/1102351.1102478 dblp:conf/icml/XuTYYK05 fatcat:yj4bffblmfhwdknayez6xscxfy