—This paper shows how to reduce evaluation time for context inference. Probabilistic Context Inference has proven to be a good representation of the physical reality with uncerta...
Korbinian Frank, Patrick Robertson, Sergio Fortes ...
Many of today's best classification results are obtained by combining the responses of a set of base classifiers to produce an answer for the query. This paper explores a nov...
This paper presents results from experiments, mathematical analysis, and simulations of a network of static and mobile sensors for detecting threats on city streets and in open ar...
One of the major weaknesses of current research on the Semantic Web (SW) is the lack of proper means to represent and reason with uncertainty. A number of recent efforts from the ...
Paulo Cesar G. da Costa, Marcelo Ladeira, Rommel N...
In this paper we present a framework for using multi-layer perceptron (MLP) networks in nonlinear generative models trained by variational Bayesian learning. The nonlinearity is h...