Gaussian processes for ordinal regression

@misc{citeulike:1693352, abstract = {We present a probabilistic kernel approach to ordinal regression based on Gaussian processes. A threshold model that generalizes the probit function is used as the likelihood function for ordinal variables. Two inference techniques, based on the Laplace approximation and the expectation propagation algorithm respectively, are derived for hyperparameter learning and model selection. We compare these two Gaussian process approaches with a previous ordinal regression method based on support...}, author = {Chu, W. and Ghahramani, Z. }, citeulike-article-id = {1693352}, keywords = {gaussian\_process, ordinal\_regression}, posted-at = {2008-04-02 09:30:50}, priority = {0}, title = {Gaussian processes for ordinal regression}, url = {http://citeseer.ist.psu.edu/734140.html}, year = {2004} }

TY - GEN ID - citeulike:1693352 L3 - citeulike-article-id:1693352 TI - Gaussian processes for ordinal regression N2 - We present a probabilistic kernel approach to ordinal regression based on Gaussian processes. A threshold model that generalizes the probit function is used as the likelihood function for ordinal variables. Two inference techniques, based on the Laplace approximation and the expectation propagation algorithm respectively, are derived for hyperparameter learning and model selection. We compare these two Gaussian process approaches with a previous ordinal regression method based on support... KW - gaussian_process KW - ordinal_regression AU - Chu, W AU - Ghahramani, Z PY - 2004/// UR - http://citeseer.ist.psu.edu/734140.html ER -