<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bartkowiak, A.</style></author><author><style face="normal" font="default" size="100%">Evelpidou, N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visualizing some multi-class erosion data using Kernel methods.</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">Proceedings in Computational Statistics</style></publisher><pages><style face="normal" font="default" size="100%">805-812</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Using a given data set (the Kefallinia erosion data) with only 3 dimensions and with fractal correlation dimension rGP ¼ 1:60, we wanted to see, what really by the kernel methods is provided.We have used Gaussian kernels with various kernel width ¾. In particular we wanted to ¯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&lt;br&gt;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.&lt;/p&gt;</style></abstract></record></records></xml>