I am a beginner in Machine Learning. I was reading through basics of SVM and read this definition:
The goal of a support vector machine is to find the optimal separating hyperplane which maximizes the margin of the training data.
Now, when I observed the examples, in a 2D plane, a straight line was being considered as a separator for data. Now, my query is that how is a straight line a hyperplane if each coordinate that describes the line is two dimensional. Unless hyperplane refers to observation of the equation that describes a curve, I dont understand how it works. If so, could someone please give a good explanation of hyperplanes.