WFRF:(Axelson Fisk Marina 1972)
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- Imberg, Henrik,1991Gothenburg University,Göteborgs universitet,Institutionen för matematiska vetenskaper,Department of Mathematical Sciences (author)
Optimal sampling in unbiased active learning
- Article/chapterEnglish2020
Publisher, publication year, extent ...
- 2020
- electronicrdacarrier
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- LIBRIS-ID:oai:research.chalmers.se:53697c5a-3025-4aa0-90a9-a6ec8b31f08b
- https://research.chalmers.se/publication/520253URI
- https://research.chalmers.se/publication/519957URI
- https://gup.ub.gu.se/publication/297633URI
Supplementary language notes
- Language:English
- Summary in:English
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Notes
- A common belief in unbiased active learning is that, in order to capture the most informative instances, the sampling probabilities should be proportional to the uncertainty of the class labels. We argue that this produces suboptimal predictions and present sampling schemes for unbiased pool-based active learning that minimise the actual prediction error, and demonstrate a better predictive performance than competing methods on a number of benchmark datasets. In contrast, both probabilistic and deterministic uncertainty sampling performed worse than simple random sampling on some of the datasets.
Subject headings and genre
- NATURVETENSKAP Matematik Sannolikhetsteori och statistik hsv//swe
- NATURAL SCIENCES Mathematics Probability Theory and Statistics hsv//eng
- Optimal design
- Weighted loss
- Sampling weights
- Generalised linear models
- Unequal probability sampling
- Active learning
- logistic-regression
- models
Added entries (persons, corporate bodies, meetings, titles ...)
- Jonasson, Johan,1966Gothenburg University,Göteborgs universitet,Institutionen för matematiska vetenskaper,Department of Mathematical Sciences(Swepub:cth)jonasson (author)
- Axelson-Fisk, Marina,1972Gothenburg University,Göteborgs universitet,Institutionen för matematiska vetenskaper,Department of Mathematical Sciences(Swepub:cth)marinaa (author)
- Göteborgs universitetInstitutionen för matematiska vetenskaper (creator_code:org_t)
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- In:Proceedings of Machine Learning Research108, s. 559-5692640-3498
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