The integration of diverse forms of informative data by learning an optimal combination of base kernels in classification or regression problems can provide enhanced performance w...
In kernel methods, an interesting recent development seeks to learn a good kernel from empirical data automatically. In this paper, by regarding the transductive learning of the k...
We present a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework. In a first step, kernel matrices on a fixed set of input points are l...
In this paper we present a simple hierarchical Bayesian treatment of the sparse kernel logistic regression (KLR) model based MacKay's evidence approximation. The model is re-p...
We apply nonparametric hierarchical Bayesian modelling to relational learning. In a hierarchical Bayesian approach, model parameters can be "personalized", i.e., owned b...
Zhao Xu, Volker Tresp, Kai Yu, Shipeng Yu, Hans-Pe...