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11

ICPR
2010
IEEE

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Gaussian Process Learning from Order Relationships Using Expectation Propagation

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Gaussian Process Learning from Order Relationships Using Expectation Propagation

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A method for Gaussian process learning of a scalar function from a set of pair-wise order relationships is presented. Expectation propagation is used to obtain an approximation to the log marginal likelihood which is optimised using an analytical expression for its gradient. Experimental results show that the proposed method performs well compared with a previous method for Gaussian process preference learning. 1

Ruixuan Wang, Stephen James Mckenna

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Computer Vision | Gaussian Process | Gaussian Process Learning | ICPR 2010 | Pair-wise Order Relationships |

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Added	02 Aug 2010
Updated	02 Aug 2010
Type	Conference
Year	2010
Where	ICPR
Authors	Ruixuan Wang, Stephen James Mckenna

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