@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}
}