Data Integration for Classification Problems Employing Gaussian Process Priors
Abstract
By adopting Gaussian process priors a fully Bayesian solution to the problem of integrating possibly heterogeneous data sets within a classification setting is presented. Approximate inference schemes employing Variational & Expectation Propagation based methods are developed and rigorously assessed. We demonstrate our approach to integrating multiple data sets on a large scale protein fold prediction problem where we infer the optimal combinations of covariance functions and achieve state-of-the-art performance without resorting to any ad hoc parameter tuning and classifier combination.
Cite
Text
Girolami and Zhong. "Data Integration for Classification Problems Employing Gaussian Process Priors." Neural Information Processing Systems, 2006.Markdown
[Girolami and Zhong. "Data Integration for Classification Problems Employing Gaussian Process Priors." Neural Information Processing Systems, 2006.](https://mlanthology.org/neurips/2006/girolami2006neurips-data/)BibTeX
@inproceedings{girolami2006neurips-data,
title = {{Data Integration for Classification Problems Employing Gaussian Process Priors}},
author = {Girolami, Mark and Zhong, Mingjun},
booktitle = {Neural Information Processing Systems},
year = {2006},
pages = {465-472},
url = {https://mlanthology.org/neurips/2006/girolami2006neurips-data/}
}