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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 548391.0, MPI für biologische Kybernetik / Biologische Kybernetik
Incremental Sparsification for Real-time Online Model Learning
Authors:Nguyen-Tuong, D.; Peters, J.
Editors:Teh, Y. W.; Titterington, M.
Date of Publication (YYYY-MM-DD):2010-05
Title of Proceedings:Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010)
Start Page:557
End Page:564
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:6505
URL:http://www.aistats.org/
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