Online GentleAdaBoost -- Technical Report
Chapman Siu
TL;DR
This paper introduces an online version of GentleAdaBoost, extending the batch boosting method to an online setting with theoretical support and empirical comparisons on benchmark datasets.
Contribution
It presents a novel online boosting algorithm for GentleAdaBoost with theoretical justifications and empirical evaluation against other online methods.
Findings
The online GentleAdaBoost effectively adapts to streaming data.
Theoretical analysis supports the convergence of the online approach.
Empirical results show competitive performance on benchmark datasets.
Abstract
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.
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Taxonomy
TopicsMachine Learning and Algorithms · Machine Learning and Data Classification · Domain Adaptation and Few-Shot Learning