Active Learning: Applying RinSCut Thresholding Strategy to Uncertainty Sampling. Conference paper. pp 922–932; Cite this conference paper. Download book PDF.
Abstract. In many supervised learning approaches to text classification, it is necessary to have a large volume of manually labeled documents to.
Active Learning: Applying RinSCut Thresholding Strategy to Uncertainty Sampling ; Publication title. AI 2003: Advances in Artificial Intelligence ; Editors. TD ...
Apr 25, 2024 · Active Learning: Applying RinSCut Thresholding Strategy to Uncertainty Sampling. Australian Conference on Artificial Intelligence 2003: 922 ...
By applying our new thresholding strategy, RinSCut, to the conventional uncertainty sampling, we propose a new framework which automatically selects informative ...
Jul 6, 2023 · In this work, we systematically examine uncertainty sampling algorithms under both stream-based and pool-based active learning. We propose a ...
Active Learning: Applying RinSCut Thresholding Strategy to Uncertainty Sampling. Conference Paper. Dec 2003; Lect Notes Comput Sci. Kang Hyuk Lee · Judy ...
This paper addresses two issues of active learning. Firstly, to solve a problem of uncertainty sampling that it often fails by selecting outliers, ...
People also ask
What is uncertainty sampling in active learning?
What is active learning in ML?
Jan 1, 2022 · In this section, we present different frameworks for measuring the learner's uncertainty in a query instance: evidence‑based uncertainty (EBU), ...
Missing: RinSCut | Show results with:RinSCut
Uncertainty sampling, a frequently utilized active learning strategy, selects instances about which the model is uncertain but it does not consider the reasons ...
Missing: RinSCut | Show results with:RinSCut