@inproceedings{6505,
  title = {Incremental Sparsification for Real-time Online Model Learning},
  journal = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010)},
  booktitle = {JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010},
  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.},
  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},
  slug = {6505},
  author = {Nguyen-Tuong, D. and Peters, J.},
  month_numeric = {5}
}