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JMLR
2010

Efficient Multioutput Gaussian Processes through Variational Inducing Kernels

11 years 2 months ago
Efficient Multioutput Gaussian Processes through Variational Inducing Kernels
Interest in multioutput kernel methods is increasing, whether under the guise of multitask learning, multisensor networks or structured output data. From the Gaussian process perspective a multioutput Mercer kernel is a covariance function over correlated output functions. One way of constructing such kernels is based on convolution processes (CP). A key problem for this approach is efficient inference.
Mauricio Alvarez, David Luengo, Michalis Titsias,
Added 19 May 2011
Updated 19 May 2011
Type Journal
Year 2010
Where JMLR
Authors Mauricio Alvarez, David Luengo, Michalis Titsias, Neil D. Lawrence
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