@InProceedings{ Garcke:2007*1,
author = {Jochen Garcke},
title = {A dimension adaptive sparse grid combination technique for
machine learning},
booktitle = { Proceedings of the 13th Biennial Computational Techniques
and Applications Conference, CTAC-2006},
year = {2007},
editor = {Wayne Read and Jay W. Larson and A. J. Roberts},
volume = {48},
series = {ANZIAM J.},
pages = {C725--C740},
abstract = {We introduce a dimension adaptive sparse grid combination
technique for the machine learning problems of
classification and regression. A function over a
$d$-dimensional space, which assumedly describes the
relationship between the features and the response
variable, is reconstructed using a linear combination of
partial functions that possibly depend only on a subset of
all features. The partial functions are adaptively chosen
during the computational procedure. This approach
(approximately) identifies the \textsc{anova}-decomposition
of the underlying problem. Experiments on synthetic data,
where the structure is known, show the advantages of a
dimension adaptive combination technique in run time
behaviour, approximation errors, and interpretability. },
annote = {other},
file = {dimAdapCTAC.pdf:http\://www.math.tu-berlin.de/~garcke/paper/dimAdapCTAC.pdf:PDF}
,
http = {http://journal.austms.org.au/ojs/index.php/ANZIAMJ/article/view/70}
,
pdf = {http://garcke.ins.uni-bonn.de/research/pub/dimAdapCTAC.pdf}
}