How SVM constructs boundaries? Math explained.
Mathematical concepts behind how SVM can separate data points in high dimensional space
Support vector machines were first introduced by Vladmir Vapnik and his colleagues at Bell Labs in 1992. However, many are not aware that basics of support vector machines were already developed in 1960s with his PhD thesis at Moscow University. Over decades, SVM has been highly preferred by many since it uses less computational resources while allowing data scientists to achieve notable accuracy. Not to mention that it solves both classification and regression problems.
1. Basic Concept
SVM can solve linear and non-linear problems and work well for many practical business problems. The principle idea of SVM is straight forward. The learning model draws a line which separates data points into multiple classes. In a binary problem, this decision boundary takes the widest street approach maximising the distance to the closest data points from each class.
In vector calculus, the dot product measures ‘how much’ one vector lies along another, and tells you the amount of force going in the direction of the displacement, or in the…
