Large Margin Distribution Machine

TL;DR

The paper introduces the Large Margin Distribution Machine (LDM), a novel approach that optimizes margin distribution rather than just the minimum margin, leading to improved generalization in classification tasks.

Contribution

LDM is a new learning method that focuses on margin distribution, providing theoretical and empirical evidence of its superiority over traditional SVM.

Findings

LDM outperforms SVM in generalization performance.

Theoretical analysis confirms the importance of margin distribution.

Empirical results demonstrate the effectiveness of LDM across datasets.

Abstract

Support vector machine (SVM) has been one of the most popular learning algorithms, with the central idea of maximizing the minimum margin, i.e., the smallest distance from the instances to the classification boundary. Recent theoretical results, however, disclosed that maximizing the minimum margin does not necessarily lead to better generalization performances, and instead, the margin distribution has been proven to be more crucial. In this paper, we propose the Large margin Distribution Machine (LDM), which tries to achieve a better generalization performance by optimizing the margin distribution. We characterize the margin distribution by the first- and second-order statistics, i.e., the margin mean and variance. The LDM is a general learning approach which can be used in any place where SVM can be applied, and its superiority is verified both theoretically and empirically in this paper.