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ICML
2005
IEEE

Dirichlet enhanced relational learning

9 years 10 months ago
Dirichlet enhanced relational learning
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.
Zhao Xu, Volker Tresp, Kai Yu, Shipeng Yu, Hans-Pe
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2005
Where ICML
Authors Zhao Xu, Volker Tresp, Kai Yu, Shipeng Yu, Hans-Peter Kriegel
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