@InProceedings{ Garcke:2010,
author = {J. Garcke},
title = {Classification with Sums of Separable Functions},
booktitle = {ECML PKDD 2010, Part I},
year = {2010},
editor = {Jos\'{e} Balc\'{a}zar and Francesco Bonchi and Aristides
Gionis and Mich\`{e}le Sebag},
volume = {6321},
series = {LNAI},
pages = {458-473},
abstract = {We present a novel approach for classification using a
discretised function representation which is independent of
the data locations. We construct the classifier as a sum of
separable functions, extending the paradigm of separated
representations. Such a representation can also be viewed
as a low rank tensor product approximation. The central
learning algorithm is linear in both the number of data
points and the number of variables, and thus is suitable
for large data sets in high dimensions. We show that our
method achieves competitive results on several benchmark
data sets which gives evidence for the utility of these
representations.},
annote = {proc_ref},
file = {sumsep_class_ecml.pdf:http\://www.math.tu-berlin.de/~garcke/paper/sumsep_class_ecml.pdf:PDF}
,
owner = {garcke},
pdf = {http://garcke.ins.uni-bonn.de/research/pub/sumsep_class_ecml.pdf}
,
seriestitle = { Lecture Notes in Artificial Intelligence},
timestamp = {2010.06.25}
}