@proceedings {12825,
	title = {Visualizing some multi-class erosion data using Kernel methods.},
	year = {2006},
	pages = {805-812},
	publisher = {Proceedings in Computational Statistics},
	abstract = {<p>Using a given data set (the Kefallinia erosion data) with only 3 dimensions and with fractal correlation dimension rGP {\textonequarter} 1:60, we wanted to see, what really by the kernel methods is provided.We have used Gaussian kernels with various kernel width {\textthreequarters}. In particular we wanted to {\textasciimacron}nd out, whether the GDA (Generalized Discrimination Analysis) as proposed by Baudat and Anouar (2000), permits to distinguish better the high, medium and low erosion classes as compared to the<br>classical Fisherian discriminant analysis. The general result is that the GDA yields discriminant variates permitting for better differentiation among groups, however the calculations are more lengthy.</p>},
	author = {Bartkowiak, A. and Evelpidou, N.}
}
