3132	 Experiments with Some Algorithms that Find Central Solutions for Pattern Classification	 In two-class pattern recognition it is a standard technique to have an algorithm finding hyperplanes which separates the two classes in a linearly separable training set. The traditional methods find a hyperplane which separates all points in the other but such a hyperplane is not necessarily centered in the empty space between the two classes. Since a central hyperplane does not favor one class or the other it should have a lower error rate in classifying new points and is therefore better than a noncentral hyperplane. Six algorithms for finding central hyperplanes are tested on three data sets. Although frequently used practice the modified relaxation algorithm is very poor. Three algorithms which are defined in the paper are found to be quite good. Pattern recognition pattern classification linear discriminants central hyperplanes centering centrality criteria dead zone hyperplane linearly separable relaxation algorithm accelerated relaxation
