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Incremental Sparsification for Real-time Online Model Learning

2010

Conference Paper

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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.

Author(s): Nguyen-Tuong, D. and Peters, J.
Book Title: JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010
Journal: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010)
Pages: 557-564
Year: 2010
Month: May
Day: 0
Editors: Teh, Y.W. , M. Titterington
Publisher: JMLR

Department(s): Empirical Inference
Research Project(s): Robot Skill Learning
Bibtex Type: Conference Paper (inproceedings)

Event Name: Thirteenth International Conference on Artificial Intelligence and Statistics
Event Place: Chia Laguna Resort, Italy

Address: Cambridge, MA, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{6505,
  title = {Incremental Sparsification for Real-time Online Model Learning},
  author = {Nguyen-Tuong, D. and Peters, J.},
  journal = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010)},
  booktitle = {JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010},
  pages = {557-564},
  editors = {Teh, Y.W. , M. Titterington},
  publisher = {JMLR},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = may,
  year = {2010},
  month_numeric = {5}
}