Large Margin Distribution Machine
This addresses a foundational issue in machine learning for improving SVM generalization, though it is incremental as it builds on existing SVM frameworks.
The paper tackles the problem that maximizing the minimum margin in SVMs does not guarantee better generalization, proposing the Large Margin Distribution Machine (LDM) to optimize margin distribution using mean and variance, which shows theoretical and empirical superiority.
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.