Support vectors are the data points that are closer to the hyperplane. These data points are important because moving them will change which location of the hyperplane maximizes the distance between points along the decision boundary, meaning that they influence the position and orientation of the hyperplane. These support vectors are used to find the maximum distance between data points of both classes (maximum margin).

It should be noted that other points in the classifier are rendered irrelevant, as only support vectors have the power to move the hyperplane.

The green linear line is the hyperplane. The distance between the black lines is the maximum margin. The data points that are circled in black are the support vectors.