@inproceedings{schwaighofer2005learning,
author = {Schwaighofer, Anton and Tresp, Volker and Yu, Kai},
title = {Learning Gaussian Process Kernels via Hierarchical Bayes},
booktitle = {Advances in Neural Information Processing Systems 17},
year = {2005},
month = {January},
abstract = {We present a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework. In a first step, kernel matrices on a fixed set of input points are learned from data using a simple and efficient EM algorithm. This step is nonparametric, in that it does not require a parametric form of covariance function. In a second step, kernel functions are fitted to approximate the learned covariance matrix using a generalized Nystroem method, which results in a complex, data driven kernel. We evaluate our approach as a recommendation engine for art images, where the proposed hierarchical Bayesian method leads to excellent prediction performance.},
publisher = {MIT Press},
url = {http://approjects.co.za/?big=en-us/research/publication/learning-gaussian-process-kernels-via-hierarchical-bayes/},
edition = {Advances in Neural Information Processing Systems 17},
}