We consider distributed estimation of a time-dependent, random state vector based on a generally nonlinear/non-Gaussian state-space model. The current state is sensed by a serial ...
The likelihood for patterns of continuous attributes for the naive Bayesian classifier (NBC) may be approximated by kernel density estimation (KDE), letting every pattern influenc...
We took an innovative approach to service level management for network enterprise systems by using integrated monitoring, diagnostics, and adaptation services in a service-oriente...
Haiqin Wang, Guijun Wang, Alice Chen, Changzhou Wa...
In this paper, we describe an interface consisting of a virtual showroom where a team of two highly realistic 3D agents presents product items in an entertaining and attractive wa...
Boris Brandherm, Helmut Prendinger, Mitsuru Ishizu...
This paper re-examines the problem of parameter estimation in Bayesian networks with missing values and hidden variables from the perspective of recent work in on-line learning [1...