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Filtered Gaussian processes for learning with large data-sets

Lookup NU author(s): Dr Jian Shi

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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 small-dimensional 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. © Springer-Verlag Berlin Heidelberg 2005.


Publication metadata

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

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

Publisher: Springer

URL: http://dx.doi.org/10.1007/978-3-540-30560-6_5

DOI: 10.1007/978-3-540-30560-6_5

Notes: book doi: 10.1007/b105497

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783540244578


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