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NIPS
2008

Local Gaussian Process Regression for Real Time Online Model Learning

9 years 7 months ago
Local Gaussian Process Regression for Real Time Online Model Learning
Learning in real-time applications, e.g., online approximation of the inverse dynamics model for model-based robot control, requires fast online regression techniques. Inspired by local learning, we propose a method to speed up standard Gaussian process regression (GPR) with local GP models (LGP). The training data is partitioned in local regions, for each an individual GP model is trained. The prediction for a query point is performed by weighted estimation using nearby local models. Unlike other GP approximations, such as mixtures of experts, we use a distance based measure for partitioning of the data and weighted prediction. The proposed method achieves online learning and prediction in real-time. Comparisons with other non-parametric regression methods show that LGP has higher accuracy than LWPR and close to the performance of standard GPR and -SVR.
Duy Nguyen-Tuong, Matthias Seeger, Jan Peters
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2008
Where NIPS
Authors Duy Nguyen-Tuong, Matthias Seeger, Jan Peters
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