In this paper, we propose a distributed learning strategy in wireless sensor networks. Taking advantage of recent developments on kernel-based machine learning, we consider a new ...
— This paper considers a network composed of robotic agents and static nodes performing spatial estimation of a dynamic physical processes. The physical process is modeled as a s...
Networks of thousands of sensors present a feasible and economic solution to some of our most challenging problems, such as real-time traffic modeling, military sensing and trackin...
In this paper, we present a variational Bayesian (VB) approach to computing the interval estimates for nonhomogeneous Poisson process (NHPP) software reliability models. This appr...
Hiroyuki Okamura, Michael Grottke, Tadashi Dohi, K...
One of the important components of granular computing is interval computations. In interval computations, at each intermediate stage of the computation, we have intervals of possi...
Martine Ceberio, Vladik Kreinovich, Andrzej Pownuk...