<XML><RECORDS><RECORD><REFERENCE_TYPE>7</REFERENCE_TYPE><REFNUM>7897</REFNUM><AUTHORS><AUTHOR>Shi,J.Q.</AUTHOR><AUTHOR>Murray-Smith,R.</AUTHOR><AUTHOR>Titterington,M.</AUTHOR><AUTHOR>Pearlmutter,B.A.</AUTHOR></AUTHORS><YEAR>2005</YEAR><TITLE>Learning with large data sets using filtered Gaussian Process priors</TITLE><PLACE_PUBLISHED>Proceedings of the Hamilton Summer School on Switching and Learning in Feedback systems, Ed. R. Murray-Smith, R. Shorten, Springer-Verlag, Lecture Notes in Computing Science, Vol. 3355 </PLACE_PUBLISHED><PUBLISHER>Springer Verlag</PUBLISHER><PAGES>p128-139</PAGES><LABEL>Shi:2005:7897</LABEL><ABSTRACT>Kernel-based non-parametric 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 data-set. Model learning and prediction are based on the filtered data, thereby decreasing the computational burden dramatically. </ABSTRACT></RECORD></RECORDS></XML>