I. Introduction
Support Vector Machine (SVM) is a machine learning algorithm that widely used for classification. SVM is one of the robust and efficient classification methods amongst the well know classification algorithms such as nearest neighbor, boosted decision trees, regularized logistic regression, neural networks, and random forests [1]–[3]. When dealing with non-linearly separable data, SVM maps the data into higher dimensional space using kernels prior to performing the classification [4]. SVM formulates a quadratic programming (QP) problem to find a separating hyperplane, which maximizes the margin between two classes of the data [3], [5], [6]. Since SVM achieves a unique solution and learns from dimensionality of feature space, it is more robust than other techniques to over fitting [4], [6], [7]. Despite all the advantages and applications of the SVM [8], [9], its classification speed is deteriorated when dealing with large scale problems as it uses large number of support vectors. In addition, its training computationally expensive and timely [10], [11]. Over the last two decades, many techniques have been proposed to speed up the test and training time of the SVM [5], [10]–[18] which have been resulted in techniques that reduce the number of SVs. However, there are demands for more powerful techniques. In some branches of control such as nonlinear [19], [20] and optimal control [21] SVM has been used due to its capabilities. However, the application of the Sliding Mode Control (SMC) to speed up the training period of the SVM and improving its performance has not been reported in the literature.