In machine learning the margin of a single data point is defined to be the distance from the data point to a decision boundary. Note that there are many distances and decision boundaries that may be appropriate for certain datasets and goals. A margin classifier is a classifier that explicitly utilizes the margin of each example while learning a classifier. There are theoretical justifications (based on the VC dimension) as to why maximizing the margin (under some suitable constraints) may be beneficial for machine learning and statistical inferences algorithms.
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“Everything that explains the world has in fact explained a world that does not exist, a world in which men are at the center of the human enterprise and women are at the margin “helping” them. Such a world does not exist—never has.”
—Gerda Lerner (b. 1920)