## 4 Description of the Machine Learning Methods
### Support Vector Machines (SVM)  
Support vector machines [40] represent a method of classification (binary classification in the standard approach) and regression. An SVM model considers the training instances as points in a multi-dimensional space, which can be transformed in order for the classes to be separated with a large margin. The idea of splitting the hyperspace into two parts can be also found in the training principle of the single-layer perceptron, for example, but in this case, it works only if the problem is linearly separable.  
For the nonlinear cases, SVM uses kernels for mapping the data into a different space with more dimensions compared with the original space, where a problem can become linearly separable even if it was not originally so. In addition, some errors in the classification of the training data can be allowed using soft margins with the goal of increasing the generalization capability.  
Support vector machines benefit from solid mathematical foundations, which offer a very good accuracy compared with other learning methods. Another advantage is the small number of parameters that the user has to choose (the type of kernel with its parameters, and a cost parameter that defines the balance between the tolerance for training errors and the generalization capability). A small disadvantage is the fact that the standard model is binary, and in order to apply it to problems with multiple classes, it is necessary to obtain several partial models that are subsequently aggregated on the basis of various strategies such as \"one-versus-all\" or \"one-versus-one\".  
The process of learning in traditional classifiers is based on the empirical risk minimization (ERM) principle. That is, the model that obtains the lowest error on the training set is chosen. One problem with this principle is that the noise in the training data is ignored; thus, in real-world problems, the ERM-based models such as the traditional artificial neural networks will learn not only the data but also the noise, leading to inaccurate predictions. Another common issue with conventional learners is that there are many parameters to be tuned. The third problem that usually occurs is that the algorithms converge only to local optima within their search space, while the global optimum is desired.  
The SVM method proposed by [PERSON] has several advantages over the other learning methods. SVM is based on the structural risk minimization principle from the computational learning theory which always converges to a global optimum, in contrast with ERM. Additionally, SVM has strong generalization capabilities. As a disadvantage, SVM models are computationally expensive; they need time and memory as the complexity of the model increases (depending on the dimension of the training data) [41].  
Consequently, SVM, as statistical learning theory-based machine learning method, gained recognition due to its features and its promising generalization performance, such as (i) the ability to model non-linear relationships; (ii) the dimensionality of the input space does not affect the generalization; and (iii) the loss function is related to a quadratic programming problem whose solution is global and, in general, unique [42].