@article{,
author={Kov{\'a}cs, Korn{\'e}l and Kocsor, Andr{\'a}s},
title={Various Hyperplane Classifiers Using Kernel Feature Spaces},
abstract={In this paper we introduce a new family of hyperplane classifiers. But,
in contrast to Support Vector Machines (SVM) - where a constrained quadratic
optimization is used - some of the proposed methods lead to the unconstrained
minimization of convex functions while others merely require solving a
linear System of equations. So that the efficiency of these methods could
be checked, classification tests were conducted on standard databases.
In our evaluation, classification results of SVM were of course used as
a general point of reference, which we found were outperformed in many
cases. },
journal={Acta Cybernetica},
volume={16},
year={2003},
pages={271-278},
month={January},
number={2}
}