@InProceedings{Supelec435,
author = {Matthieu Geist and Olivier Pietquin and Gabriel Fricout},
title = {{Online Bayesian Kernel Regression from Nonlinear Mapping of Observations}},
year = {2008},
booktitle = {{Proceedings of the 18th IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2008)}},
number = {a53},
pages = {309-314},
month = {October},
address = {Cancun (Mexico)},
url = {http://hal-supelec.archives-ouvertes.fr/hal-00335052/en/},
abstract = {In a large number of applications, engineers have to estimate
a function linked to the state of a dynamic system. To do so,
a sequence of samples drawn from this unknown function
is observed while the system is transiting from state to state
and the problem is to generalize these observations to unvisited
states. Several solutions can be envisioned among
which regressing a family of parameterized functions so as
to make it fit at best to the observed samples. However classical
methods cannot handle the case where actual samples
are not directly observable but only a nonlinear mapping of
them is available, which happen when a special sensor has
to be used or when solving the Bellman equation in order to
control the system. This paper introduces a method based
on Bayesian filtering and kernel machines designed to solve
the tricky problem at sight. First experimental results are
promising.}
}