  Support Vector Machines (SVMs) are still one of the most popular and precise
classifiers. The Radial Basis Function (RBF) kernel has been used in SVMs to
separate among classes with considerable success. However, there is an
intrinsic dependence on the initial value of the kernel hyperparameter. In this
work, we propose OKSVM, an algorithm that automatically learns the RBF kernel
hyperparameter and adjusts the SVM weights simultaneously. The proposed
optimization technique is based on a gradient descent method. We analyze the
performance of our approach with respect to the classical SVM for
classification on synthetic and real data. Experimental results show that OKSVM
performs better irrespective of the initial values of the RBF hyperparameter.
