# Optimal Margin Distribution Machine

arXiv: https://arxiv.org/abs/1604.03348 · HTML: https://mlsanity.com/paper/1604.03348/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2016.

## Built on by (most cited first, 10 of 10)

- [Contrastive Bayesian Analysis for Deep Metric Learning](https://mlsanity.com/paper/2210.04402/) (2022)
- [Recent Advances in Large Margin Learning](https://mlsanity.com/paper/2103.13598/) (2021)
- [Multi-class Support Vector Machine with Maximizing Minimum Margin](https://mlsanity.com/paper/2312.06578/) (2023)
- [Coded Residual Transform for Generalizable Deep Metric Learning](https://mlsanity.com/paper/2210.04180/) (2022)
- [Scalable Optimal Margin Distribution Machine](https://mlsanity.com/paper/2305.04837/) (2023)
- [Pairwise Margin Maximization for Deep Neural Networks](https://mlsanity.com/paper/2110.04519/) (2021)
- [On the Optimization of Margin Distribution](https://mlsanity.com/paper/2204.14118/) (2022)
- [Modular Grammatical Evolution for the Generation of Artificial Neural Networks](https://mlsanity.com/paper/2208.02787/) (2022)
- [MaxMin-L2-SVC-NCH: A Novel Approach for Support Vector Classifier Training and Parameter Selection](https://mlsanity.com/paper/2307.07343/) (2023)
- [Structure-Preserving Margin Distribution Learning for High-Order Tensor Data with Low-Rank Decomposition](https://mlsanity.com/paper/2509.14577/) (2025)

## Abstract (arXiv)

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. Based on this idea, we propose a new method, named Optimal margin Distribution Machine (ODM), 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 proposed method is a general learning approach which can be used in any place where SVM can be applied, and their superiority is verified both theoretically and empirically in this paper.