Bayesian learning, widely used in many applied data-modeling problems, is often accomplished with approximation schemes because it requires intractable computation of the posterio...
Abstract. Many reinforcement learning domains are highly relational. While traditional temporal-difference methods can be applied to these domains, they are limited in their capaci...
Trevor Walker, Lisa Torrey, Jude W. Shavlik, Richa...
Abstract. This paper introduces an efficient way of representing textures using connected regions which are formed by coherent multi-scale over-segmentations. We show that the rece...
Background: Genome sequencing projects have expanded the gap between the amount of known protein sequences and structures. The limitations of current high resolution structure det...
Kasper Stovgaard, Christian Andreetta, Jesper Ferk...
Systems based on statistical and machine learning methods have been shown to be extremely effective and scalable for the analysis of large amount of textual data. However, in the r...