# A Review of Machine Learning and IoT in Smart Transportation
## 2 Machine Learning and IoT in Smart Transportation
### Machine Learning Algorithms
#### 2.1.10 Non-Probabilistic Linear Classification  
Depending on the type of a classification problem, one can use either a probabilistic or a non-probabilistic approach. When the probabilities play no apparent role in the data classification, non-probabilistic methods are usually preferred. Apart from the problem type, there are other parameters to consider that can lead to a non-probabilistic solution. Non-probabilistic classification is useful when dealing with structured data. Moreover, an important aspect to consider is whether cluster construction or assignment is needed in the respective classification problem [57].  
We have seen in [35], the comparison of _k_-NN, RF, and Support Vector Machine (SVM) in order to identify road surface abnormalities. Linear SVM, as well as the other two classifiers, performed very well for this task, with a CCR Rate over 99%. SVM is a binary classifier used to separate labeled data in an optimum way using a hyperplane. The hyperplane needs to have a maximum distance from the data of both classes. The data points at that are closer to the hyperplane are called support vectors because they play a significant role in the hyperplane's orientation and position. SVM has also been met in [37], dealing with the task of road accident detection. The SVM algorithm was compared to an ANN and an RF Method. The results indicated that the SVM had better Specificity than the RF but the least good accuracy of the three algorithms. The last method found in the research for traffic congestion predictions [23] is SVM. In this study, it was compared to a decision tree, an RF, an MLP and a Logistic Regression algorithm. SVM performed equally well with the other algorithms, slightly outperformed by the Logistic Regression. The MRF algorithm was discussed earlier, in the research about a parking space detection system [14], as a way to correct possible conflicts in the SVM classification. This research a modified version of the classic binary SVM. A one-against-one strategy enables the implementation of a multi-class SVM classifier. So, SVM takes image patches as input data and attempts to classify the detected parking spaces to 8 possible classes. The SVM kernel function is a Radial Basis Function (RBF) adoption. The purpose of [38] is to create a dataset with images from parking lots, collected from HD cameras, located near parking lots (PKLot). To test the dataset capabilities, an SVM classifier was used. Two texture based features where used: Local Binary Patterns (LBP) and Local Phase Quantization (LPQ). The best kernel for the SVM problem was found to be Gaussian. The results were evaluated using the Overall Error Rate (OER) metric. Two SVM versions are also applied in [24]. The research goal is the automatic detection of street elements, as discussed earlier. A Deep Belief Network is applied in order to extract the best features and then a classifier is used to separate the elements. Apart from the \(k\)-NN, an SVM with a quadratic and an SVM with a Gaussian kernel were used offering satisfactory results.