@article {13202,
	title = {Efficient kernel machines using the improved fast Gauss transform},
	journal = {Advances in neural information processing systems},
	volume = {17},
	year = {2005},
	month = {2005///},
	pages = {1561 - 1568},
	abstract = {The computation and memory required for kernel machines with N train-         ing samples is at least O(N 2). Such a complexity is significant even for
         moderate size problems and is prohibitive for large datasets. We present
         an approximation technique based on the improved fast Gauss transform
         to reduce the computation to O(N ). We also give an error bound for the
         approximation, and provide experimental results on the UCI datasets.
},
	author = {Yang,C. and Duraiswami, Ramani and Davis, Larry S.}
}
