We present a new approach to dealing with default information based on the theory of belief functions. Our semantic structures, inspired by Adams' -semantics, are epsilon-beli...
Salem Benferhat, Alessandro Saffiotti, Philippe Sm...
In recent years there has been a flurry of works on learning probabilistic belief networks. Current state of the art methods have been shown to be successful for two learning scen...
In this paper, we present a novel approach to enhance hierarchical phrase-based machine translation systems with linguistically motivated syntactic features. Rather than directly ...
Abstract. Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. However, the availability of comprehensive, reliable and suitable manual segmen...
Tammy Riklin Raviv, Koen Van Leemput, William M. W...
The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we ext...