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.
Famous quotes containing the word margin:
“Will not a tiny speck very close to our vision blot out the glory of the world, and leave only a margin by which we see the blot? I know no speck so troublesome as self.”
—George Eliot [Mary Ann (or Marian)