PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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

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 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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:2964
Deposited By:Mark Girolami
Deposited On:08 March 2007