29 Nov 2020

Support Vector Machines

→ A Support Vector Machine algorithm’s objective is to find a hyperplane (or in the case of 2D a line) that classifies data points. SVMs can be used for both regression and classification.

→ Support vectors are data points that are closer to the hyperplane and influence the position and orientation of the hyperplane. Using these support vectors, we maximize the margin of the classifier. Deleting the support vectors will change the position of the hyperplane. These are the points that help us build our SVM.