This paper proposes the use of uncertainty reduction in machine learning methods such as co-training and bilingual bootstrapping, which are referred to, in a general term, as ‘c...
We present a method for representing and reasoning with uncertainty in RDF(S) and OWL ontologies based on Bayesian networks. Categories and Subject Descriptors: I.2.4 Artificial I...
Symbolic non-deterministic planning represents action effects as sets of possible next states. In this paper, we move toward a more probabilistic uncertainty model by distinguishi...
Rune M. Jensen, Manuela M. Veloso, Randal E. Bryan...
This paper addresses robust deconvolution filtering when the system and noise dynamics are obtained by parametric system identification. Consistent with standard identification me...
A pervasive problem in large relational databases is identity uncertainty which occurs when multiple entries in a database refer to the same underlying entity in the world. Relati...