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.