Incremental Sparsification for Real-time Online Model Learning
2010
Conference Paper
ei
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 |
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 |
Links: |
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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}, doi = {}, month_numeric = {5} } |