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Local Gaussian Process Regression for Real Time Online Model Learning

Duy Nguyen-Tuong; Matthias W. Seeger; Jan Peters
In: Daphne Koller; Dale Schuurmans; Yoshua Bengio; Léon Bottou (Hrsg.). Advances in Neural Information Processing Systems 21, Proceedings of the Twenty-Second Annual Conference on Neural Information Processing Systems. Neural Information Processing Systems (NeurIPS-2008), December 8-11, Vancouver, British Columbia, Canada, Pages 1193-1200, Curran Associates, Inc. 2008.


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 ν-SVR.

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