Support Vector Machine Algorithm
The support vector machine (SVM) algorithm is a classifier that finds the hyperplane that effectively separates the observations into their class labels. It starts by positioning each instance into a data space with n dimensions, where n represents the number of features. Next, it traces an imaginary line that clearly separates the instances belonging to a class label from the instances belonging to others.
A support vector refers to the coordinates of a given instance. According to this, the support vector machine is the boundary that effectively segregates the different support vectors in a data space.
For a two-dimensional data space, the hyperplane is a line that splits the data space into two sections, each one representing a class label.
How Does It Work?
The following diagram shows a simple example of an SVM model. Both the green and orange dots represent the instances from the input dataset, where the colors define the class label to which each instance...
