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  Incremental Sparsification for Real-time Online Model Learning

Nguyen-Tuong, D., & Peters, J. (2010). Incremental Sparsification for Real-time Online Model Learning. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010), 557-564.

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 Urheber:
Nguyen-Tuong, D1, Autor           
Peters, J1, 2, Autor           
Teh M. Titterington, Y.W., Herausgeber
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 Zusammenfassung: Online model learning in real-time is required by many applications such as in robot tracking control. It poses a difficult problem, as fast and incremental online regression with large data sets is the essential component which cannot be achieved by straightforward usage of off-the-shelf machine learning methods (such as Gaussian process regression or support vector regression). In this paper, we propose a framework for online, incremental sparsification with a fixed budget designed for large scale real-time model learning. The proposed approach combines a sparsification method based on an independence measure with a large scale database. In combination with an incremental learning approach such as sequential support vector regression, we obtain a regression method which is applicable in real-time online learning. It exhibits competitive learning accuracy when compared with standard regression techniques. Implementation on a real robot emphasizes the applicability of the proposed approach in real-time online model learning for real world systems.

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Sprache(n):
 Datum: 2010-05
 Publikationsstatus: Erschienen
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 Ort, Verlag, Ausgabe: -
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 Art der Begutachtung: -
 Identifikatoren: URI: http://www.aistats.org/aistats2010/
BibTex Citekey: 6505
 Art des Abschluß: -

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Titel: Thirteenth International Conference on Artificial Intelligence and Statistics
Veranstaltungsort: Chia Laguna Resort, Italy
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Titel: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010)
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Cambridge, MA, USA : JMLR
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 557 - 564 Identifikator: -