SVM Classification
Support Vector Machine Classification

Mathematically, SVM finds hyperplane in an n-dimensional space(n number of features) that can segregate data points into different classes. It is found by maximizing the margin (distance) between the classes.

Support vectors are closed data points to the hyperplane, that can influence the position and orientation of the hyperplane, and helps maximize the margin between classes. The dimensions of the hyperplane depend upon the number of input features.