# Online Multiclass Boosting

arXiv: https://arxiv.org/abs/1702.07305 · HTML: https://mlsanity.com/paper/1702.07305/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2017.

## Built on by (most cited first, 7 of 7)

- [A Boosting Approach to Reinforcement Learning](https://mlsanity.com/paper/2108.09767/) (2021)
- [Boosting for Online Convex Optimization](https://mlsanity.com/paper/2102.09305/) (2021)
- [Efficient Methods for Online Multiclass Logistic Regression](https://mlsanity.com/paper/2110.03020/) (2021)
- [On Classification-Calibration of Gamma-Phi Losses](https://mlsanity.com/paper/2302.07321/) (2023)
- [Online Agnostic Multiclass Boosting](https://mlsanity.com/paper/2205.15113/) (2022)
- [Sample-Efficient Agnostic Boosting](https://mlsanity.com/paper/2410.23632/) (2024)
- [Sample-Optimal Agnostic Boosting with Unlabeled Data](https://mlsanity.com/paper/2503.04706/) (2025)

## Abstract (arXiv)

Recent work has extended the theoretical analysis of boosting algorithms to multiclass problems and to online settings. However, the multiclass extension is in the batch setting and the online extensions only consider binary classification. We fill this gap in the literature by defining, and justifying, a weak learning condition for online multiclass boosting. This condition leads to an optimal boosting algorithm that requires the minimal number of weak learners to achieve a certain accuracy. Additionally, we propose an adaptive algorithm which is near optimal and enjoys an excellent performance on real data due to its adaptive property.