Complex Support Vector Machines For Quaternary Classification
P. Bouboulis, E. Theodoridou, S. Theodoridis

We present a support vector machines (SVM) rationale suitable
for quaternary classification problems that use complex
data, exploiting the notions of widely linear estimation and
pure complex kernels. The recently developed Wirtinger"s
calculus on complex RKHS is employed in order to compute
the Lagrangian and derive the dual optimization problem. We
show that this approach is equivalent with solving two real
SVM tasks exploiting a specific real kernel, which is induced
by the chosen complex kernel.