ICASSP
2011
IEEE
13 years 4 months ago
2011
IEEE
The Generalized State Coherence Transform (GSCT) has been recently proposed as an efficient tool for the estimation of multidimensional TDOA of multiple sources. The transform de...
PAMI
2010
13 years 11 months ago
2010
In this paper, we present a new Adaptive Scale Kernel Consensus (ASKC) robust estimator as a generalization of the popular and state-of-the-art robust estimators such as RANSAC (R...
CSDA
2008
14 years 8 days ago
2008
Multivariate kernel density estimation provides information about structure in data. Feature significance is a technique for deciding whether features
SIAMCO
2008
14 years 19 days ago
2008
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 ...
MLMTA
2007
14 years 2 months ago
2007
- Though prediction of protein secondary structures has been an active research issue in bioinformatics for quite a few years and many approaches have been proposed, a new challeng...
BCS
2008
14 years 2 months ago
2008
In this work, a new algorithm is proposed for fast estimation of nonparametric multivariate kernel density, based on principal direction divisive partitioning (PDDP) of the data s...
ICPR
2010
IEEE
14 years 6 months ago
2010
IEEE
—We propose a new method for online estimation of probabilistic discriminative models. The method is based on the recently proposed online Kernel Density Estimation (oKDE) framew...
HUMO
2007
Springer
14 years 6 months ago
2007
Springer
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...
IJCNN
2007
IEEE
14 years 7 months ago
2007
IEEE
— Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression tec...
IJCNN
2008
IEEE
14 years 7 months ago
2008
IEEE
— 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...