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This learning method requires a supervisor that tells the system what is the expected output for each input. Then, the machine learns from this knowledge. Specifically, the learning algorithm is given labeled data and the corresponding output. The machine learns a function that maps a given input to an appropriate output. For example, if we provide the ML system during the training phase with different pictures of cars, and with information indicating that these are pictures of cars, it will be able to build a model that can distinguish the cars’ pictures from any other pictures. The quality of a supervised model depends on the difference between the predicted output and the exact output. The convergence speed of supervised learning is high although it requires large amount of labeled data [6]. Next, we discuss the well-known supervised learning algorithms.

Figure 2.2 Machine learning types.

Figure 2.3 Illustration of SVM.

Support Vector Machine

Support Vector Machine (SVM) algorithm is a linear supervised binary classifier. It separates data points using a hyperplane. The best hyperplane is the one which results in maximum separation between the two given classes. It is called the maximum margin hyperplane. SVM is considered to be a stable algorithm applied for binary classification. For multiple classification problems, the classification tasks must be reduced to multiple binary classification problems. The basic principle of SVM is illustrated in Figure 2.3.

K-Nearest Neighbors

A non-parametric learning algorithm used for classification and regression. The algorithm does not require any assumption on the data distribution. The objective of KNN is to decide the class of a data point based on the results of majority voting of its K-nearest neighbors (KNNs). The idea is that a data point is identified to belong to a certain class if KNNs belong to that class. A weight can be used for each neighbor that is proportional to the inverse of its distance to the data point in the classification process. KNN is easy to implement, not sensitive to outliers, highly accurate, and easily calculates features. It is also suitable for multi-class classification applications. The basic principle of KNN is illustrated in Figure 2.4.