This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
Perceptual experiments indicate that corners and curvature are very important features in the process of recognition. This paper presents a new method to detect rotational symmetr...
Abstract— Recently, several wireless sensor network studies demonstrated large discrepancies between experimentally observed communication properties and properties produced by w...
Alberto Cerpa, Jennifer L. Wong, Louane Kuang, Mio...
Machine-learning algorithms are employed in a wide variety of applications to extract useful information from data sets, and many are known to suffer from superlinear increases in ...
Karthik Nagarajan, Brian Holland, Alan D. George, ...
This paper proposes and experimentally validates a Bayesian network model of a range finder adapted to dynamic environments. All modeling assumptions are rigorously explained, and...
Tinne De Laet, Joris De Schutter, Herman Bruyninck...