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  Local Gaussian Process Regression for Real Time Online Model Learning and Control

Nguyen-Tuong, D., Seeger, M., & Peters, J. (2009). Local Gaussian Process Regression for Real Time Online Model Learning and Control. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), Advances in neural information processing systems 21 (pp. 1193-1200). Red Hook, NY, USA: Curran.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C493-C Versions-Permalink: https://hdl.handle.net/21.11116/0000-0002-DE5F-B
Genre: Konferenzbeitrag

Urheber

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 Urheber:
Nguyen-Tuong, D1, 2, Autor           
Seeger, M1, 2, Autor           
Peters, J1, 2, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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Schlagwörter: -
 Zusammenfassung: 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 nonparametric regression methods show that LGP has higher accuracy than LWPR and close to the performance of standard GPR and nu-SVR.

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Sprache(n):
 Datum: 2009-06
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: BibTex Citekey: 5410
 Art des Abschluß: -

Veranstaltung

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Titel: Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008)
Veranstaltungsort: Vancouver, BC, Canada
Start-/Enddatum: 2008-12-08 - 2008-12-10

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Titel: Advances in neural information processing systems 21
Genre der Quelle: Konferenzband
 Urheber:
Koller, D, Herausgeber
Schuurmans, D, Herausgeber
Bengio, Y, Herausgeber
Bottou, L, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: Red Hook, NY, USA : Curran
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 1193 - 1200 Identifikator: ISBN: 978-1-60560-949-2