Gender determination using a Support Vector Machine Variant

In this paper a modified class of Support Vector Machines (SVMs) inspired from the optimization of Fisher´s discriminant ratio is presented. Moreover, we present a novel class of nonlinear decision surfaces by solving the proposed optimization problem in arbitrary Hilbert spaces defined by Mercer´s kernels. The effectiveness of the proposed approach is demonstrated by comparing it with the standard SVMs and other classifiers, like Kernel Fisher Discriminant Analysis (KFDA) in gender determination.