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Abstract: Gaussian process classifiers (GPCs) are fully statistical kernel classification models derived from Gaussian processes for regression. In GPCs, the probability of belonging to a certain class at an input location is monotonically related to the value of some latent function at that location. Starting from a prior over this latent function, the data are used to infer both the posterior over the latent function and the values of hyperparameters determining various aspects of the function. GPCs... (Update)
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BibTeX entry: (Update)
H. Kim and Z. Ghahramani. The EM-EP algorithm for Gaussian process classification. In Proc. of the Workshop on Probabilistic Graphical Models for Classification (at ECML), 2003. http://citeseer.ist.psu.edu/kim03emep.html More
@misc{ kim03emep,
author = "H. Kim and Z. Ghahramani",
title = "The EM-EP algorithm for Gaussian process classification",
text = "H. Kim and Z. Ghahramani. The EM-EP algorithm for Gaussian process classification.
In Proc. of the Workshop on Probabilistic Graphical Models for Classification
(at ECML), 2003.",
year = "2003",
url = "citeseer.ist.psu.edu/kim03emep.html" }
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