Support Vector Machine (SVM) is a classifier that was fully introduced by Vapnik in [122,123], however, it was first mentioned in [124]. The standard SVM is a binary linear classifier, i.e., it can separate the samples from the two classes only and only when they are linearly separable. SVM tries to find an optimal separating hyperplane. It is a hyperplane that distinguishes elements of the two different classes in an efficient way. What exactly we mean by optimal and efficient is described later in this chapter. The equation describing the hyperplane is calculated using the samples from the training data set. This means that some noisy samples can affect the result of the classification. To avoid it, the so-called soft margin approach was proposed by Cortes and Vapnik in [125]. This approach is presented in one of the sections in the chapter.