The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Selective Sampling Using the Query by Committee Algorithm
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Valiant, L. G. (1984) · Communications of the ACM · doi:10.1145/1968.1972 · Cited by 3,279
Cited by
(2009) · Minds at UW (University of Wisconsin) · Cited by 4,791
Ensemble learning for data stream analysis: A survey
Krawczyk, B., Minku, L. L., Gama, J., Stefanowski, J., & Woźniak, M. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.004 · Cited by 1,050
Active Learning for Convolutional Neural Networks: A Core-Set Approach
Sener, O., & Savarese, S. (2017) · arXiv · doi:10.48550/arxiv.1708.00489 · Cited by 922
Settles, B. (2012) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00429ed1v01y201207aim018 · Cited by 605
Deep learning in microscopy image analysis: a survey
Xing, F., Xie, Y., Su, H., Liu, F., & Yang, L. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2766168 · Cited by 460
van Grinsven, M. J. J. P., van Ginneken, B., Hoyng, C. B., Theelen, T., & Sanchez, C. I. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2526689 · Cited by 426
Active learning for biomedical citation screening
Wallace, B. C., Small, K., Brodley, C. E., & Trikalinos, T. A. (2010) · Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1835804.1835829 · Cited by 123
Explainable Active Learning (XAL): Toward AI Explanations as Interfaces for Machine Teachers
Ghai, B., Liao, Q. V., Zhang, Y., Bellamy, R., & Mueller, K. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3432934 · Cited by 106
Query-by-committee improvement with diversity and density in batch active learning
Kee, S., del Castillo, E., & Runger, G. (2018) · Information Sciences · doi:10.1016/j.ins.2018.05.014 · Cited by 98
(2012) · Cited by 98
Semi-Supervised Classification of Network Data Using Very Few Labels
Lin, F., & Cohen, W. W. (2010) · 2010 International Conference on Advances in Social Networks Analysis and Mining · doi:10.1109/asonam.2010.19 · Cited by 96
Experience sampling for building predictive user models: a comparative study
Kapoor, A., & Horvitz, E. (2008) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1357054.1357159 · Cited by 88
Incremental learning of event definitions with Inductive Logic Programming
Katzouris, N., Artikis, A., & Paliouras, G. (2015) · Machine Learning · doi:10.1007/s10994-015-5512-1 · Cited by 62
Confidence-based classifier design
Li, M., & Sethi, I. K. (2006) · Pattern Recognition · doi:10.1016/j.patcog.2006.01.010 · Cited by 61
Principles of Lifelong Learning for Predictive User Modeling
Kapoor, A., & Horvitz, E. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-73078-1_7 · Cited by 28
Active learning for e-rulemaking: public comment categorization
(2008) · Scholarship @ Cornell Law (Cornell University) · doi:10.5555/1367832.1367873 · Cited by 13
(2021) · Cited by 6
TBAL: Two-stage batch-mode active learning for image classification
Shen, Y., Song, Y., Wu, C.-H., & Kuo, C.-C. J. (2022) · Signal Processing: Image Communication · doi:10.1016/j.image.2022.116731 · Cited by 6
(2000) · CrossRef Listing of Deleted DOIs · doi:10.1162/153244302760185243
Metadata
- Authors
- Freund, Y., Seung, H. S., Shamir, E., & Tishby, N.
- Venue
- Machine Learning
- Published
- 1997-08
- Volume
- 28
- Issue
- 2-3
- Pages
- 133-168
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1528361845
- Cited by
- 1,097
- References
- 1
- FWCI
- 10.46
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
