## 3 The Application of Conventional ML Algorithms
### Support Vector Machine (SVM)  
The support vector machine (SVM) is a machine learning method used for classification and regression analysis, which was originally proposed by [PERSON] et al. in the 1990s. The SVM method maps the data points in the sample space to a high-dimensional space, where an optimal hyperplane is identified to maximally separate the data points of different classes. The SVM classifier is shown in Figure 5. This hyperplane is known as the separating hyperplane. The \"support vectors\" in SVM are the data points that are closest to the separating hyperplane, which determine the position and orientation of the hyperplane. The primary objective of SVM is to maximize the distance between the support vectors and the separating hyperplane, i.e., the classification margin, while ensuring the accuracy of the classification. This distance is called the margin, and SVM aims to identify the hyperplane with the largest margin. When the data are not linearly separable, SVM can perform nonlinear classification in a high-dimensional space by utilizing a kernel function to map the data points to a higher-dimensional space. SVM is one of the most popular classifiers due to its excellent generalization capability. In 2008, [PERSON] et al. applied generic algorithm (GA) in the detection of the underground target, then proposed a method consisting of a feature extraction strategy aimed at the dielectric characteristics of the target and a classifier based on SVM to recognize the material of buried objects [7]. In 2013, [PERSON] et al. also used the discrete cosine transform  
Figure 5: SVM classification model. (DCT) features of GPR data and SVM for material classification and compared it to other conventional methods [8]. In the same year, [PERSON], [PERSON] et al. used SVM algorithm to identify concrete internal voids and studied the accuracy of four kernel functions [9]. In 2018, a study used the H-Alpha decomposition (a polarimetric decomposition) to extract the feature and trained an SVM classifier [10]. [PERSON] et al. developed a COSVM classifier and, different from these mentioned methods based on B-scan data, they took some points of A-scan data as a feature vector for detection of a landmine [11]. In addition, it is proven in this work that COSVM kept a balance between the classification accuracy and the cost of training time, compared with one-class SVM (OSVM), Mahalanobis one-class SVM (MOSVM), and two-class SVM. In the same year, [PERSON], [PERSON] et al. proposed a method to distinguish targets by energy and perform mine identification [12]. Since SVM is widely used in the detection and classification of GPR targets and regarded as a highly adaptable method, it plays an important role in the comparative research of various feature extraction strategies. A study compared the performance of feature extraction based on histogram of oriented gradients (HOG) and n-Row average subtraction (nRAS) with SVM and K-NN classifiers [13]. In addition, in the tests with SVM, 3 Ras performed better in the positive object discrimination ratio (POD) than nRAS and the original image cropped without any normalization. Presented in [14] is another study made by the same group as [13]; they proposed a novel fast feature extraction method that converted the interest region into a feature vector (FV) and combined it with nRAS and background removal technique (BRT). They compared these two fusions of methods, which were named as nR-FV and BRT-FV, respectively, with FV, nRAS, and HOG. When implemented with SVM, 3 RAS remained the best performance, while nR-FV had less feature generation time. In 2017, [PERSON] et al. made a comparison of feature representation methods shown in Table 1 for automatic buried threat detection (BTD) [15]. In this work, feature extraction methods, which had been applied to BTD with GPR or were potentially promising, were divided into three categories: gradient feature extraction methods, binary comparison methods, and pixel representations.