Large margin distribution learning

Abstract

Support vector machines (SVMs) and Boosting are possibly the two most popular learning approaches during the past two decades. It is well known that the margin is a fundamental issue of SVMs, whereas recently the margin theory for Boosting has been defended, establishing a connection between these two mainstream approaches. The recent theoretical results disclosed that the margin distribution rather than a single margin is really crucial for the generalization performance, and suggested to optimize the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. Inspired by this recognition, we advocate the large margin distribution learning, a promising research direction that has exhibited superiority in algorithm designs to traditional large margin learning.