PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Efficient Nonparametric Bayesian Modelling with Sparse Gaussian Process Approximations
matthias seeger, Neil Lawrence and Ralf Herbrich
(2006) Technical Report. Max Planck Institute.


Sparse approximations to Bayesian inference for nonparametric Gaussian Process models scale linearly in the number of training points, allowing for the application of powerful kernel-based models to large datasets. We present a general framework based on the {\em informative vector machine} (IVM) \citep{Lawrence:02a} and show how the complete Bayesian task of inference and learning of free hyperparameters can be performed in a practically efficient manner. Our framework allows for arbitrary likelihood and kernel functions, so that a large number of elementary models can be treated in a unified way. We present a range of experiments for our method applied to binary classification and regression tasks. Models based on a single latent function can be combined in order to address more complicated setups. We demonstrate this approach for a multi-way classification model.

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EPrint Type:Monograph (Technical Report)
Additional Information:Available at
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:2699
Deposited By:matthias seeger
Deposited On:19 December 2007