# Tight Risk Bounds for Multi-Class Margin Classifiers

arXiv: https://arxiv.org/abs/1507.03040 · HTML: https://mlsanity.com/paper/1507.03040/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2015.

## Built on by (most cited first, 4 of 4)

- [Generalization Error Bounds for Multiclass Sparse Linear Classifiers](https://mlsanity.com/paper/2204.06264/) (2022)
- [Long-term drought prediction using deep neural networks based on geospatial weather data](https://mlsanity.com/paper/2309.06212/) (2023)
- [Large-Scale Sequential Learning for Recommender and Engineering Systems](https://mlsanity.com/paper/2205.06893/) (2022)
- [Unlock the Power of Algorithm Features: A Generalization Analysis for Algorithm Selection](https://mlsanity.com/paper/2405.11349/) (2024)

## Abstract (arXiv)

We consider a problem of risk estimation for large-margin multi-class classifiers. We propose a novel risk bound for the multi-class classification problem. The bound involves the marginal distribution of the classifier and the Rademacher complexity of the hypothesis class. We prove that our bound is tight in the number of classes. Finally, we compare our bound with the related ones and provide a simplified version of the bound for the multi-class classification with kernel based hypotheses.