Online GentleAdaBoost -- Technical Report
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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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 2 of its 10 references that are in the map. First posted on arXiv 27 Aug 2023.
Builds on
2 of 10 references.
References · 2
- 2020River: machine learning for streaming data in Pythoncited by 96
- 2019AMF: Aggregated Mondrian Forests for Online Learningcited by 17
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