What is Support Vector Machine?
Support Vector Machine is popular Supervised Machine Learning Technique used both classification and regression problems. It got more attraction in 1990’s.
The key idea of SVM(Support Vector Machine) is hyperplane(in n-dimension) that separates the +ve (Positive) points from -ve (Negative) points as “Widely as Possible”.
The hyperplane that separates the +ve points from -ve points as “Widely as Possible” is called as Margin Maximization Hyperplane.
Positive and Negative hyperplane are parallel to Margin Maximization Hyperplane, such that the distance between Positive Hyperplane to Margin Maximization Hyperplane and Negative Hyperplane to Margin Maximization Hyperplane is equal.
The points which pass through Positive hyperplane and Negative hyperplane are called as Support Vectors.
Therefor, Support Vector Machine try to find a Hyperplane that maximizes the margin.
