Data Integration for Classification Problems Employing Gaussian Process Priors
Mark Girolami and Mingjun Zhong
In: NIPS 2006, 7 Dec 2006, Vancouver.
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 and 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.