## 3 The Application of Conventional ML Algorithms
### Hidden Markov Model (HMM)  
In contrast to a normal Markov model, in a hidden Markov model, the state is not directly visible but some variables affected by the state are. Each state has a probability distribution over the possible output symbols. So, the sequence of output symbols can reveal some information about the state sequence. Hidden Markov model is a model based on probability calculation; it is a special dynamic Bayesian network model; it is a double random process composed of general random process and Markov chain. Different from SVM and K-NN, HMM produces time sequence of random observations as a function of the state. The word hidden in HMM comes from the attribute that the actual state is hidden or not observable. Given that an observation vector is observed at a certain time, there are probabilities that the process is in each state. The actual state of the process is hidden. In other words, each state is represented by a probability density function, which can either be continuous or discrete. If time sequences are known, the hidden state can be eliminated. Figure 6: K-NN classification model. In 1999, [PERSON] et al. introduced HMM into the GPR detection for the first time; it was used to model the time-varying signatures caused by the landmines as the vehicle moved [21]. Two models are established in this paper, one for background and the other for mines. Discrete HMM was used in the experiment, the Viterbi algorithm is used to look back at the model state, the fuzzy clustering algorithm is used to find the state, and the Baum-Welch algorithm is used to estimate the model parameters. Then, in 2001, they made an expansion of the previous work in testing and evaluation of methods and used both discrete and continuous HMM [22]. The B-scan images were divided into slices, and their feature vectors were 16-dimension vectors defined by the values of the positive or negative diagonal and antidiagonal arrays in the neighborhoods of their respective maxima. The discrete HMM was trained by the Baum-Welch (BW) algorithm and the continuous HMM was initialized using clustering methods and learned using the BW algorithm. The continuous HMM performed slightly better than the discrete HMM, and the fusion of the two resulted in a better performance than the individual algorithms. In 2005, [PERSON] et al. made progress in that they developed a complete real-time software system for landmine detection based on continuous HMM and applied it to data acquired from outdoor test sites [23]. Two corrective training scenarios were used to improve the overall performance by about 10%. Then, [24] used Gabor filter as a feature extraction strategy instead of the feature vectors in [22], which assumed that the signatures of targets had a rising edge or a falling edge with a certain orientation. In 2011, [PERSON] et al. applied the multi-stream discrete HMM (MSDHMM) to the classification of landmines [25]. The core idea of MSDHMM is the fusion of different sensors or different features and classifiers of one sensor. In this work, they made use of a fusion of Gabor and EHD and adopted classifiers based on distance and possibility, respectively. The result of this study showed that the structure of MSDHMM improved the ability of discrimination of HMM. Ground-penetrating radar systems achieved high detection rates but at the expense of high false alarm rates. In 2014, [PERSON] et al. used a combination of SVM and HMM with down sampling, a preprocessing step that is unbiased and suitable for real-time analysis and detection [26]. The HMM is utilized as a pre-screener that evaluates all traces of the file sequentially and the SVM is subsequently used as a confirmer that evaluates only those traces selected by the HMM. The combined HMM-SVM method retains all of the correct classifications by the SVM and reduces the false positive rate to 0.0007. Receiver operating characteristic (ROC) curve is a co-ordinate diagram composed of false positive rate as the horizontal axis and hit probability as the vertical axis.