The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Active learning using pre-clustering
Open accessAbstract
The paper is concerned with two-class active learning. While the common approach for collecting data in active learning is to select samples close to the classification boundary, better performance can be achieved by taking into account the prior data distribution. The main contribution of the paper is a formal framework that incorporates clustering into active learning. The algorithm first constructs a classifier on the set of the cluster representatives, and then propagates the classification decision to the other samples via a local noise model. The proposed model allows to select the most representative samples as well as to avoid repeatedly labeling samples in the same cluster. During the active learning process, the clustering is adjusted using the coarse-to-fine strategy in order to balance between the advantage of large clusters and the accuracy of the data representation. The results of experiments in image databases show a better performance of our algorithm compared to the current methods.
Overview
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
Finding groups in data: an introduction to cluster analysis
(1991) · Choice Reviews Online · doi:10.5860/choice.28-4558 · Cited by 10,474
Text classification from labeled and unlabeled documents using EM
Nigam, K., Mccallum, A. K., Thrun, S., & Mitchell, T. (2000) · Machine Learning · doi:10.1023/a:1007692713085 · Cited by 2,736
A sequential algorithm for training text classifiers
Lewis, D., & Gale, W. A. (1994) · arXiv (Cornell University) · doi:10.5555/188490.188495 · Cited by 1,731
Support vector machine active learning for image retrieval
Tong, S., & Chang, E. (2001) · Proceedings of the ninth ACM international conference on Multimedia · doi:10.1145/500141.500159 · Cited by 1,328
Editorial: On Machine Learning
Langley, P. (1986) · Machine Learning · doi:10.1023/a:1022687019898 · Cited by 951
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
Roy, N., & McCallum, A. (2001) · Cited by 833
Less is More: Active Learning with Support Vector Machines
Schohn, G., & Cohn, D. A. (2000) · Cited by 773
Employing EM and Pool-Based Active Learning for Text Classification
McCallum, A., & Nigam, K. (1998) · Cited by 689
Learning from Labeled and Unlabeled Data
(2017) · Encyclopedia of Machine Learning and Data Mining · doi:10.1007/978-1-4899-7687-1_100253 · Cited by 462
Cluster Kernels for Semi-Supervised Learning
Chapelle, O., Weston, J., & Schölkopf, B. (2002) · Cited by 461
Cited by
(2009) · Minds at UW (University of Wisconsin) · Cited by 4,791
Learning Loss for Active Learning
Yoo, D., & Kweon, I. S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00018 · Cited by 740
The Power of Ensembles for Active Learning in Image Classification
Beluch, W. H., Genewein, T., Nurnberger, A., & Kohler, J. M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00976 · Cited by 655
Settles, B. (2012) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00429ed1v01y201207aim018 · Cited by 605
Integrating Machine Learning with Human Knowledge
Deng, C., Ji, X., Rainey, C., Zhang, J., & Lu, W. (2020) · iScience · doi:10.1016/j.isci.2020.101656 · Cited by 243
Metadata
- Authors
- Nguyen, H. T., & Smeulders, A.
- Venue
- Twenty-first international conference on Machine learning - ICML '04
- Publisher
- ACM Press
- Published
- 2004
- Pages
- 79
- OpenAlex
- W1978633512
- Cited by
- 658
- References
- 20
- FWCI
- 6.51
- Type
- conference paper
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Active learning using pre-clustering. https://app.cochat.ai/papers/10.1145/1015330.1015349
- The paper
- Nguyen, H. T., & Smeulders, A. (2004). Active learning using pre-clustering. Twenty-first international conference on Machine learning - ICML '04. https://doi.org/10.1145/1015330.1015349
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.
