24 search results - page 1 / 5 » Sparse multiscale gaussian process regression |
ICML
2008
IEEE
14 years 5 months ago
2008
IEEE
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their computations on a set of m basis functions that are the covariance function of th...
JMLR
2010
12 years 11 months ago
2010
We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsify the spectral representation of the GP. This leads to a simple, practical algo...
ECCV
2008
Springer
14 years 6 months ago
2008
Springer
We present a new Gaussian Process inference algorithm, called Online Sparse Matrix Gaussian Processes (OSMGP), and demonstrate its merits with a few vision applications. The OSMGP ...
NIPS
2008
13 years 6 months ago
2008
We present a sparse approximation approach for dependent output Gaussian processes (GP). Employing a latent function framework, we apply the convolution process formalism to estab...
IJCNN
2006
IEEE
13 years 11 months ago
2006
IEEE
Abstract— This paper considers the basis vector selection issue invloved in forward selection algorithms to sparse Gaussian Process Regression (GPR). Firstly, we re-examine a pre...