Real-Life Applications of Support Vector Machines

SVMs depends on supervised learning algorithms. The aim of using SVM is to correctly classify unseen data. SVMs have a number of applications in several fields. It classifies the parts of the image as face and non-face. It contains training data of n x n pixels with a two-class face ( 1) and non-face (-1).