Hierarchical spatial data structures provide a means for organizing data for efficient processing. Most spatial data structures are optimized for performing queries, such as inters...
Elena Jakubiak Hutchinson, Sarah F. Frisken, Ronal...
Abstract. We analyze special random network models – so-called thickened trees – which are constructed by random trees where the nodes are replaced by local clusters. These obj...
Michael Drmota, Bernhard Gittenberger, Reinhard Ku...
We introduce a parallelized version of tree-decomposition based dynamic programming for solving difficult weighted CSP instances on many cores. A tree decomposition organizes cost ...
In this paper, a new learning framework?probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the pro...
We characterise a computational model for processing annotated parse trees. The model is basically rewriting-based with specific provisions for dealing with annotations along the...