@InProceedings{Supelec425,
author = {Matthieu Geist and Olivier Pietquin and Gabriel Fricout},
title = {{A Sparse Nonlinear Bayesian Online Kernel Regression}},
year = {2008},
booktitle = {{Proceedings of the 2nd IEEE International Conference on Advanced Engineering Computing and Applications in Sciences (AdvComp 2008)}},
volume = {I},
pages = {199-204},
month = {October},
note = {(best paper award)},
address = {Valencia (Spain)},
url = {http://hal-supelec.archives-ouvertes.fr/hal-00327081/en/},
abstract = {In a large number of applications, engineers have to estimate
values of an unknown function given some observed samples. This
task is referred to as function approximation or as
generalization. One way to solve the problem is to regress a
family of parameterized functions so as to make it fit at best
the observed samples.
Yet, usually batch methods are used and parameterization is
habitually linear. Moreover, very few approaches try to
quantify
uncertainty reduction occurring when acquiring more samples
(thus
more information), which can be quite useful depending on the
application. In this paper we propose a sparse nonlinear
bayesian online kernel regression. Sparsity is achieved in a
preprocessing step by using a dictionary method. The nonlinear
bayesian kernel regression can therefore be considered as
achieved online by a Sigma Point Kalman Filter. First
experiments
on a cardinal sine regression show that our approach is
effective.}
}