Support Vector Machine Sample Clauses

Support Vector Machine. Machine learning methods have shown promising results in travel mode choice analysis (Xxxx et al., 2015, Xxxxxx and Xxxxx, 2016; Xxxxxxxxxxxxx et al., 2019). Support Vector Machine (SVM) is one of the machine learning methods that have gained considerable attention in recent years (Xxxxxxxxx and Xxxxxxx 2017, Xxxxxx 2015). In its simplest form, SVM relies on the fact that in a binary-labeled data, there exists an optimum linear boundary (also known as a hyperplane in an n-dimensional space) that fully separates the two different classes. The term “optimum” refers to the situation where the distance between the boundary and the closest points from each class to the boundary is maximized. In technical terms, such distance is usually called the “margin”, while the closest points from each class are referred to as “support vectors” (Figure 22).