Support vector machines, commonly known as SVMs, are another class of machine learning algorithm that are used to classify data into one or another category using a concept called hyperplane, which is used to demarcate a linear boundary between points.
For instance, given a set of black and white points on an x-y axis, we can find multiple lines that will separate them. The line, in this case, represents the function that delineates the category that each point belongs to. In the following image, lines H1 and H2 both separate the points accurately. In this case, how can we determine which one of H1 and H2 would be the optimal line?:
Intuitively, we can say the line that is closest to the points - for instance, the ...
