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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.