We show how improved sequences for magnetic resonance imaging can be found through optimization of Bayesian design scores. Combining approximate Bayesian inference and natural ima...
Matthias W. Seeger, Hannes Nickisch, Rolf Pohmann,...
We present a FrameNet-based semantic role labeling system for Swedish text. As training data for the system, we used an annotated corpus that we produced by transferring FrameNet ...
This paper introduces a system, called PolyCluster, which adopts state-of-the-art algorithms for data visualization and integrates human domain knowledge into the construction pro...
A wrapped feature selection process is proposed in the context of robust clustering based on Laplace mixture models. The clustering approach we consider is a generalization of the...
This paper proposes a hierarchical Bayesian model that can be used for semi-supervised hyperspectral image unmixing. The model assumes that the pixel reflectances result from linea...
Nicolas Dobigeon, Jean-Yves Tourneret, Chein-I Cha...