# Multi-class Support Vector Machine with Maximizing Minimum Margin

arXiv: https://arxiv.org/abs/2312.06578 · HTML: https://mlsanity.com/paper/2312.06578/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 41 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [Large-Margin Softmax Loss for Convolutional Neural Networks](https://mlsanity.com/paper/1612.02295/) (2016)
- [Top-k Multiclass SVM](https://mlsanity.com/paper/1511.06683/) (2015)
- [Weak-shot Fine-grained Classification via Similarity Transfer](https://mlsanity.com/paper/2009.09197/) (2020)
- [Optimal Margin Distribution Machine](https://mlsanity.com/paper/1604.03348/) (2016)
- [Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction](https://mlsanity.com/paper/1607.06996/) (2016)

## Built on by (most cited first, 6 of 6)

- [Achieving More with Less: A Tensor-Optimization-Powered Ensemble Method](https://mlsanity.com/paper/2408.02936/) (2024)
- [Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image Clustering](https://mlsanity.com/paper/2404.05211/) (2024)
- [When Images Look Right and Retrieve Wrong: Coverage-Guided Cross-Scale Re-Indexing for Knowledge-Faithful Generative Perception](https://mlsanity.com/paper/2608.20810/) (2026)
- [Multi-Class Support Vector Machine with Differential Privacy](https://mlsanity.com/paper/2510.04027/) (2025)
- [Efficient and Noise-Tolerant PAC Learning of Multiclass Linear Classifiers](https://mlsanity.com/paper/2605.18662/) (2026)
- [pSVM: Soft-margin SVMs with p-norm Hinge Loss](https://mlsanity.com/paper/2408.09908/) (2024)

## Abstract (arXiv)

Support Vector Machine (SVM) stands out as a prominent machine learning technique widely applied in practical pattern recognition tasks. It achieves binary classification by maximizing the "margin", which represents the minimum distance between instances and the decision boundary. Although many efforts have been dedicated to expanding SVM for multi-class case through strategies such as one versus one and one versus the rest, satisfactory solutions remain to be developed. In this paper, we propose a novel method for multi-class SVM that incorporates pairwise class loss considerations and maximizes the minimum margin. Adhering to this concept, we embrace a new formulation that imparts heightened flexibility to multi-class SVM. Furthermore, the correlations between the proposed method and multiple forms of multi-class SVM are analyzed. The proposed regularizer, akin to the concept of "margin", can serve as a seamless enhancement over the softmax in deep learning, providing guidance for network parameter learning. Empirical evaluations demonstrate the effectiveness and superiority of our proposed method over existing multi-classification methods.