@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}
		  
}