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Learning multi-label scene classification
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Citations
Cited by
Deep Learning for Extreme Multi-label Text Classification
Liu, J., Chang, W.-C., Wu, Y., & Yang, Y. (2017) · Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3077136.3080834 · Cited by 676
Machine learning in medical applications: a review of state-of-the-art methods
Shehab, M., Abualigah, L., Shambour, Q., Abu-Hashem, M. A., Shambour, M. K. Y., Alsalibi, A. I., & Gandomi, A. H. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105458 · Cited by 542
Presence-Only Geographical Priors for Fine-Grained Image Classification
Aodha, O. M., Cole, E., & Perona, P. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00969 · Cited by 149
A Novel Approach for Multi-Label Chest X-Ray Classification of Common Thorax Diseases
Allaouzi, I., & Ben Ahmed, M. (2019) · IEEE Access · doi:10.1109/access.2019.2916849 · Cited by 126
Suri, J. S., Bhagawati, M., Paul, S., Protogerou, A. D., Sfikakis, P. P., Kitas, G. D., Khanna, N. N., Ruzsa, Z., Sharma, A. M., Saxena, S., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12030722 · Cited by 55
Knowing what you know: valid and validated confidence sets in multiclass and multilabel prediction
Cauchois, M., Gupta, S., & Duchi, J. (2020) · arXiv · doi:10.48550/arxiv.2004.10181 · Cited by 31
Predictive Learning in the Presence of Heterogeneity and Limited
Karpatne, A., Khandelwal, A., Boriah, S., & Kumar, V. (2014) · Proceedings of the 2014 SIAM International Conference on Data Mining · doi:10.1137/1.9781611973440.29 · Cited by 25
Human-AI Collaboration with Bandit Feedback
Gao, R., Saar-Tsechansky, M., De-Arteaga, M., Han, L., Lee, M. K., & Lease, M. (2021) · arXiv · doi:10.48550/arxiv.2105.10614 · Cited by 23
Cost-effective Batch-mode Multi-label Active Learning
Gui, X., Lu, X., & Yu, G. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.063 · Cited by 22
Deep learning for Amazon satellite image analysis
Bragilevsky, L., & Bajic, I. V. (2017) · 2017 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) · doi:10.1109/pacrim.2017.8121895 · Cited by 17
Metadata
- Authors
- Boutell, M. R., Luo, J., Shen, X., & Brown, C. M.
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2004-09
- Volume
- 37
- Issue
- 9
- Pages
- 1757-1771
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2156935079
- Cited by
- 2,296
- References
- 0
- FWCI
- 12.88
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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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.