Conference Publication Details
Mandatory Fields
Shi, JQ;Murray-Smith, R;Titterington, DM;Pearlmutter, BA
SWITCHING AND LEARNING IN FEEDBACK SYSTEMS
Filtered Gaussian processes for learning with large data-sets
2005
January
Published
1
4 ()
Optional Fields
128
139
Kernel-based non-paxametric models have been applied widely over recent years. However, the associated computational complexity imposes limitations on the applicability of those methods to problems with large data-sets. In this paper we develop a filtering approach based on a Gaussian process regression model. The idea is to generate a smalldimensional set of filtered data that keeps a high proportion of the information contained in the original large dataset. Model learning and prediction are based on the filtered data, thereby decreasing the computational burden dramatically.
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