# Machine-Learning-Based Methods for Acoustic Emission Testing: A Review
## 5 Comparison between ML-Based Methods for AE
The algorithm calculates the probabilities for each factor using the previous formula and selects the result with the highest probability (the maximum a posterior probability). While the estimation of the a priori probabilities is quite simple, if we do not have elements, we can hypothesize the equiprobable classes; the knowledge of conditional densities is possible only in theory. We often make assumptions about the shape of the distributions and learn the fundamental parameters from the training set. * Support vector machine: It is used in various fields, including facial recognition, text, or image classification. The algorithm works by dividing the data into different classes by finding a dividing line between the different classes (usually called hyperplane). This line is not casually taken: it is the one that maximizes the distance between the various classes in the case of more than one. In this way, the greater the distance, the greater the accuracy of the model. A hyperplane is a subspace of dimension n-1 with respect to the space in which it is contained: therefore, if we are talking about points in a 2D space, the hyperplane will be a straight line; in a 3D space, it will be a plane; and so on. In the optimal case, there is a hyperplane that completely separates the points of the two classes: this does not happen, so adjustments are applied to it in the form of soft margins or kernel tricks. In the first case, the model is granted a margin of error, that is, one or more points of a class can be found in the other class. In the second case, we find a nonlinear hyperplane, applying suitable transformations to the initial features. The support vector machine is also used for more complex models (nonlinear SVMs). In this case, it is not possible to separate the training data using a hyperplane but through a kernel function, which helps us to model nonlinear models of larger dimensions. * K-nearest neighbors (KNN): This is a supervised learning algorithm in that, unlike the other classifiers, it does not involve the creation of a model, but the training phase simply consists of memorizing the values assumed by the characteristics and labels. Its operation is based on the calculation of the distance between the record whose label you want to predict and the K elements of the dataset closest to it. The record will be labeled based on the labels of the selected K neighbors. Since the algorithm is based on the concept of distance, it is important to normalize the data in the preprocessing phase so that its measurement is not dominated by one of the attributes present in the dataset. The object to be classified is graphically represented in a three-dimensional space based on the attributes it possesses and is then classified according to its surroundings; it is assigned the class to which the majority of the closest K samples belong. * Ensemble methods: With ensemble, we mean a set of basic learning machines whose predictions are combined to improve the overall performance. The variety of terms with which the various machines are called in the literature reflects the absence of a unified theory on ensemble methods and the fact that it is a research field yet to be explored in many respects. The ensemble algorithms are made up of several basic classifiers combined with each other, according to the philosophy that a combination of classifiers provides better results than the single one. There are different types of ensembles: bagging uses a combination of weak models, each of which learns from a subset of the initial data. The final prediction is none other than the average (or the majority vote) of the output of the various ensemble models of this type, for example, random forest, composed of n decision trees. In voting, a simplified version of bagging is used but which, however, allows us to combine the results of different categories of classifiers. Boosting combines the results of individually weak models.