This article addresses the stopping criterion issue of active learning, and presents four simple stopping criteria based on confidence estimation over the ...
Firstly, this paper makes a comprehensive analy- sis on some confidence-based stopping criteria. (Zhu and Hovy, 2007), including max- confidence, min-error and ...
In 2008, Vlachos proposed a stopping criterion that stops when the confidence on a set of examples consistently drops. [23]. The logic in this stopping method ...
Comparing with the confidence-based stopping criteria proposed by. Zhu and Hovy (2007), experimental results show that the new proposed stopping criterion ...
Comparing with the confidence-based stopping criteria proposed by. Zhu and Hovy (2007), experimental results show that the new proposed stopping criterion ...
People also ask
When to stop active learning?
Why is active learning used in data annotation?
Confidence-based stopping criteria for active learning for data annotation ... Multi-Criteria-Based Strategy to Stop Active Learning for Data Annotation.
Apr 2, 2022 · We discuss confidence-based stopping methods in greater detail in section III. Stabilizing Predictions (SP) stops AL when the agreement between ...
Oct 15, 2023 · We introduce a Deep Learning Based Active Learning (DLBAL) method that may incrementally learn from a small number of annotated training samples to build an ...
Missing: criteria | Show results with:criteria
Oct 14, 2022 · This work enables practitioners to employ active learning by providing actionable recommendations for which stopping criteria are best for a given real-world ...
The simplest approach is to evaluate the convergence of learning by monitoring the generalization error of the prediction model using the test dataset. However, ...