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Shi, Jian Qing, Murray-Smith, Roderick, Titterington, D. Mike and Pearlmutter, Barak A. (2005) Filtered Gaussian Processes for Learning with Large Data-Sets. Lecture Notes in Computer Science (3355). pp. 128-139. ISSN 0302-9743
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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 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.
| Item Type: | Article |
|---|---|
| Additional Information: | Proceedings of Switching and Learning in Feedback Systems: European Summer School on Multi-Agent Control, Maynooth, Ireland, September 8-10 2003. The original publication is available at www.springerlink.com |
| Keywords: | Filtering transformation, Gaussian process regression model, Karhunen-Loeve expansion; Kernel-based non-parametric models; Principal component analysis; |
| Academic Unit: | Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: | 2511 |
| Identification Number: | DOI: 10.1007/978-3-540-30560-6_5 |
| Depositing User: | Hamilton Editor |
| Date Deposited: | 27 Apr 2011 14:56 |
| Journal or Publication Title: | Lecture Notes in Computer Science |
| Publisher: | Springer Verlag |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/2511 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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