We propose a new family of probabilistic description logics (DLs) that, in contrast to most existing approaches, are derived in a principled way from Halpern’s probabilistic fi...
Interfaces based on recognition technologies are used extensively in both the commercial and research worlds. But recognizers are still error-prone, and this results in human perf...
Jennifer Mankoff, Scott E. Hudson, Gregory D. Abow...
The explosive growth in the biomedical literature has made it difficult for researchers to keep up with advancements, even in their own narrow specializations. In addition, this c...
Previous work on context-specific independence in Bayesian networks is driven by a common goal, namely to represent the conditional probability tables in a most compact way. In th...
In this paper we propose a novel spatial associative classifier method based on a multi-relational approach that takes spatial relations into account. Classification is driven by s...