We investigate the asymptotic properties of a recursive kernel density estimator associated with the driven noise of a linear regression in adaptive tracking. We provide an almost ...
In this paper, we suggest to model priors on human motion by means of nonparametric kernel densities. Kernel densities avoid assumptions on the shape of the underlying distribution...
Thomas Brox, Bodo Rosenhahn, Daniel Cremers, Hans-...
A situation where training and test samples follow different input distributions is called covariate shift. Under covariate shift, standard learning methods such as maximum likeli...
Abstract— We present a general method to evaluate RF BuiltIn Self-Test (BIST) techniques during the design stage. In particular, the adaptive kernel estimator is used to construc...
Haralampos-G. D. Stratigopoulos, Jeanne Tongbong, ...