Details

Autor(en) / Beteiligte

• Shi, Jian Qing
• Murray-Smith, Roderick
• Titterington, D. Mike
• Pearlmutter, Barak A.
• Shorten, Robert

Titel

Filtered Gaussian Processes for Learning with Large Data-Sets

Ist Teil von

• Switching and Learning in Feedback Systems, p.128-139

Ort / Verlag

Berlin, Heidelberg: Springer Berlin Heidelberg

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.