2.3.2. Support Vector Machine (SVM) Algorithm
A supervised learning technique that generates input-output mapping functions from a collection of labeled training data is the Support Vector Machine (SVM). The mapping function may be a function of classification or a function of regression. Nonlinear kernel functions are also used for classification to translate input data to a high-dimensional feature space where the input data becomes more separable than the original input space. Hyperplanes of maximum-margin are then formed. Only a subset of the training data near the class boundaries depends on the generated model. In the present study, kernel type was radial basis polynomial, regularization parameter (C) was ten, gamma was 0.1, and stopping criteria was 1.0E (-3). The hyperparameters of the SVM algorithm was tune using a modified sequential minimal optimization (SMO) method (11, 13).