60 search results - page 3 / 12 » State-Space Inference and Learning with Gaussian Processes |
ICML
2008
IEEE
14 years 7 months ago
2008
IEEE
Stationarity is often an unrealistic prior assumption for Gaussian process regression. One solution is to predefine an explicit nonstationary covariance function, but such covaria...
ICML
2008
IEEE
14 years 7 months ago
2008
IEEE
Point processes are difficult to analyze because they provide only a sparse and noisy observation of the intensity function driving the process. Gaussian Processes offer an attrac...
JMLR
2010
13 years 1 months ago
2010
Interest in multioutput kernel methods is increasing, whether under the guise of multitask learning, multisensor networks or structured output data. From the Gaussian process pers...
ICML
2010
IEEE
13 years 7 months ago
2010
IEEE
This paper proposes a multiple instance learning (MIL) algorithm for Gaussian processes (GP). The GP-MIL model inherits two crucial benefits from GP: (i) a principle manner of lea...
IJCV
2008
13 years 6 months ago
2008
The recent development of Sequential Monte Carlo methods (also called particle filters) has enabled the definition of efficient algorithms for tracking applications in image sequen...