@conference {12826,
	title = {Visualization of five erosion risk classes using Kernel discriminants.},
	booktitle = {13th International Multi Conference {\textquoteleft}ACS Artificial Intelligence, Biometrics and Information Technology Security Computer Information Systems and Industrial Management Applications{\textquoteright}},
	year = {2006},
	abstract = {<p>Kernel discriminants are greatly appreciated because 1) they may express nonlinear boundaries between classes and 2) they permit to visualize graphically the data points belonging to di{\textregistered}erent classes. One such method is called GDA (Generalized Discriminant Analysis), however it operates on a kernel matrix of size NxN and is for large N prohibitive. We illustrate how this method works in a real situation when dealing with relatively large data. We consider a set of predictors of erosion risk in the Kefallinia island categorized into 5 classes of erosion risk (together N=3422 data items). We argue that a proper preparation of appropriate learning samples can greatly speed up the evaluations and result in good generalization properties. Our concern is to {\textasciimacron}nd appropriate data for learning. This is done by a kind of sieve algorithm.</p>},
	author = {Bartkowiak, A. and Evelpidou, N. and Vassilopoulos, A.}
}
