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Filtered Gaussian processes for learning with large data-sets

Lookup NU author(s): Dr Jian Shi


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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.

Publication metadata

Author(s): Shi JQ, Murray-Smith R, Titterington DM, Pearlmutter BA

Editor(s): Murray-Smith, R., Shorten, R.

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Switching and Learning in Feedback Systems: European Summer School on Multi-Agent Control

Year of Conference: 2005

Pages: 128-139

ISSN: 0302-9743 (Print) 1611-3349 (Online)

Publisher: Springer

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783540244578