How to measure uncertainty in uncertainty sampling for active learning
From MaRDI portal
Publication:2127220
DOI10.1007/s10994-021-06003-9OpenAlexW3173017111MaRDI QIDQ2127220
Mohammad Hossein Shaker, Vu-Linh Nguyen, Eyke Hüllermeier
Publication date: 20 April 2022
Published in: Machine Learning (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10994-021-06003-9
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Reliable classification: learning classifiers that distinguish aleatoric and epistemic uncertainty
- Optimised probabilistic active learning (OPAL)
- Selective sampling for nearest neighbor classifiers
- The naive credal classifier
- Evidence-based uncertainty sampling for active learning
- Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
- An introduction to the imprecise Dirichlet model for multinomial data
- Sequential Quadratic Programming Methods
- PROBABILITY INTERVALS: A TOOL FOR UNCERTAIN REASONING
- On the Foundations of Statistical Inference
- Upper Probabilities Based Only on the Likelihood Function
- Nearest neighbor pattern classification
This page was built for publication: How to measure uncertainty in uncertainty sampling for active learning