TRAINING A SUPPORT VECTOR MACHINE CLASSIFIER

Machine Learning (ML) is a very important field that enable researchers to develop many computer-based applications that can be used to facilitate employer's carrier in many scientific area. Support vector machine (SVM) is one of the most popular supervised learning algorithms in ML that are used to classify data. In this paper, researchers have trained a support vector machine classifier over linearly and nonlinearly separable data set. Over the nonlinearly separable dataset to types of kernels has been used, polynomial kernel and radial-basis function (RBF) kernel. The polynomial kernel reached the peak accuracy of 96% at degree of 10 and no additional capacity control. However, RBF kernel reached the peak accuracy of 96% at RBF sigma of 1 and misclassification error of 10. Moreover, cross validation technique has been used to improve and measure the performance of the nonlinear classifier