The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Improved Boosting Algorithms Using Confidence-rated Predictions
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
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Self-citation
Cited by
Top 10 algorithms in data mining
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., et al. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-007-0114-2 · Cited by 5,667
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Shan, C., Gong, S., & McOwan, P. W. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2008.08.005 · Cited by 2,300
Classification in the Presence of Label Noise: A Survey
Frenay, B., & Verleysen, M. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2292894 · Cited by 1,777
Boosting and Differential Privacy
Dwork, C., Rothblum, G. N., & Vadhan, S. (2010) · 2010 IEEE 51st Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2010.12 · Cited by 784
Learning to associate: HybridBoosted multi-target tracker for crowded scene
Li, Y., Huang, C., & Nevatia, R. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206735 · Cited by 633
Discovering Activities to Recognize and Track in a Smart Environment
Rashidi, P., Cook, D. J., Holder, L. B., & Schmitter-Edgecombe, M. (2011) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2010.148 · Cited by 462
Bagging and Boosting Classification Trees to Predict Churn
Lemmens, A., & Croux, C. (2006) · Journal of Marketing Research · doi:10.1509/jmkr.43.2.276 · Cited by 411
Signatures of a Statistical Computation in the Human Sense of Confidence
Sanders, J. I., Hangya, B., & Kepecs, A. (2016) · Neuron · doi:10.1016/j.neuron.2016.03.025 · Cited by 253
Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit?
Zhu, S., Dong, X., & Su, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00506 · Cited by 153
3D facial expression recognition based on automatically selected features
Tang, H., & Huang, T. S. (2008) · 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2008.4563052 · Cited by 140
Ramírez-Gallego, S., Fernández, A., García, S., Chen, M., & Herrera, F. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.10.001 · Cited by 140
Regionlets for Generic Object Detection
Wang, X., Yang, M., Zhu, S., & Lin, Y. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2389830 · Cited by 107
Weather Recognition Based on Images Captured by Vision System in Vehicle
Yan, X., Luo, Y., & Zheng, X. (2009) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-01513-7_42 · Cited by 92
Compressive Sequential Learning for Action Similarity Labeling
Qin, J., Liu, L., Zhang, Z., Wang, Y., & Shao, L. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2508600 · Cited by 55
Akhter, I., Jalal, A., & Kim, K. (2021) · 2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST) · doi:10.1109/ibcast51254.2021.9393293 · Cited by 58
Quantum computing and supervised machine learning
Oneto, L., Ridella, S., & Anguita, D. (2017) · Quantum Inspired Computational Intelligence · doi:10.1016/b978-0-12-804409-4.00002-4 · Cited by 11
A Simple and Interactive Transformer for Fine-Grained Emotion Detection
Sui, D., Li, B., Yang, H., Chen, Y., Liu, K., Chu, D., & Zhao, J. (2025) · IEEE Transactions on Audio, Speech and Language Processing · doi:10.1109/taslp.2024.3487418 · Cited by 2
Metadata
- Authors
- Schapire, R. E., & Singer, Y.
- Venue
- Machine Learning
- Published
- 1999-12
- Volume
- 37
- Issue
- 3
- Pages
- 297-336
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W4244952642
- Cited by
- 1,971
- References
- 1
- FWCI
- 22.79
- 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.