SVM’s were initially developed in 1960s then they were refined in 1990s and now they are becoming very popular in machine learning as they are demonstrating that they are very powerful and different from other Machine Learning algorithms.

A Support Vector Machine (SVM) is a supervised machine learning algorithm that can be employed for both classification and regression purposes. They are more commonly used in classification problems.

How SVM’s Work?

Consider some usual points on a 2 dimensional space with two columns x1 & x2.

Now how can we derive a line that will separate these two different points and classify them separately? This separation or decision boundary is compulsory as when we add new points in future that we want to classify haven’t been classified yet. We will get to know whether they will fall either in Green area or Red area.

So how to separate these points?

Well there can be numerous ways of drawing lines in between that will achieve the same result as shown.

But we want to find the most optimal line that’s what SVM’s are all about. SVM’ are about finding the best decision boundary that will help us to separate out space into classes.

So lets find out how the SVM’s searches for it. The required line is searched through Maximum Margin.

We can see a line that separates these two classes of points and it has the Maximum Margin which means that the distance between the line and each of these points (touching Red and Green point) is equidistant.

Now sum of these two distances has to be maximized in order for this line to be SVM. The boundary points are know as Support Vectors. Why So?

Basically these two vectors are supporting whole algorithm rest other points don’t contribute to the result of algorithm, only these two points are contributing, therefore they are called Supporting Vectors.

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