— A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experi...
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 ...
A new method of kernel density estimation with a varying adaptive window size is proposed. It is based on the socalled intersection of confidence intervals (ICI) rule. Several exa...
We propose a novel semi-supervised method for building a statistical model that represents the relationship between sounds and text labels (“tags”). The proposed method, named...
Jun Takagi, Yasunori Ohishi, Akisato Kimura, Masas...
Recent work on background subtraction has shown developments on two major fronts. In one, there has been increasing sophistication of probabilistic models, from mixtures of Gaussi...
Manjunath Narayana, Allen R. Hanson, Erik G. Learn...