This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
Construction of complex software systems with largely off-the-shelf components has become a reality with the wide availability and acceptance of component frameworks and distribut...
William J. McIver Jr., Karim Keddara, Christian Oc...
We consider requests for capacity in a given tree network T = (V, E) where each edge of the tree has some integer capacity ue. Each request consists of an integer demand df and a ...
Chandra Chekuri, Marcelo Mydlarz, F. Bruce Shepher...
The relational model, as proposed by Codd, contained the concept of relations as tables composed of tuples of single valued attributes taken from a domain. In most of the early lit...
This paper shows how to formally characterize language learning in a finite parameter space as a Markov structure, hnportant new language learning results follow directly: explici...