1900 search results - page 23 / 380 » Gaussian Processes for Machine Learning |
NIPS
2003
13 years 9 months ago
2003
We generalise the Gaussian process (GP) framework for regression by learning a nonlinear transformation of the GP outputs. This allows for non-Gaussian processes and non-Gaussian ...
CVPR
2006
IEEE
14 years 9 months ago
2006
IEEE
We advocate the use of Gaussian Process Dynamical Models (GPDMs) for learning human pose and motion priors for 3D people tracking. A GPDM provides a lowdimensional embedding of hu...
ICML
2006
IEEE
14 years 8 months ago
2006
IEEE
We investigate under what conditions clustering by learning a mixture of spherical Gaussians is (a) computationally tractable; and (b) statistically possible. We show that using p...
TSD
2010
Springer
13 years 5 months ago
2010
Springer
The main idea of a priori machine learning is to apply a machine learning method on a machine learning problem itself. We call it "a priori" because the processed data se...
DIS
2008
Springer
13 years 9 months ago
2008
Springer
Abstract. An important task in many scientific and engineering disciplines is to set up experiments with the goal of finding the best instances (substances, compositions, designs) ...