# Online GentleAdaBoost -- Technical Report

arXiv: https://arxiv.org/abs/2308.14004 · HTML: https://mlsanity.com/paper/2308.14004/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 27 Aug 2023.

## Builds on (most cited first, 2 of 2)

- [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020)
- [AMF: Aggregated Mondrian Forests for Online Learning](https://mlsanity.com/paper/1906.10529/) (2019)

## Abstract (arXiv)

We study the online variant of GentleAdaboost, where we combine a weak learner to a strong learner in an online fashion. We provide an approach to extend the batch approach to an online approach with theoretical justifications through application of line search. Finally we compare our online boosting approach with other online approaches across a variety of benchmark datasets.