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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. Cambridge, MA, USA: JMLR.

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 Creators:
Nguyen-Tuong, D1, 2, Author              
Peters, J1, 2, Author              
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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 Abstract: 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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 Dates: 2010-05
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 6505
 Degree: -

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Title: Thirteenth International Conference on Artificial Intelligence and Statistics (AI & Statistics 2010)
Place of Event: Chia Laguna Resort, Italy
Start-/End Date: 2010-05-13 - 2010-05-15

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Title: JMLR Workshop and Conference Proceedings
Source Genre: Series
 Creator(s):
Teh, YW, Editor
Titterington, M, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : JMLR
Pages: - Volume / Issue: 9 Sequence Number: - Start / End Page: 557 - 564 Identifier: -