The DOI matches its Crossref record.
A sequential algorithm for training text classifiers: corrigendum and additional data
Abstract
article Free Access Share on A sequential algorithm for training text classifiers: corrigendum and additional data Author: David D. Lewis AT&T Bell Laboratories, Murray Hill, NJ AT&T Bell Laboratories, Murray Hill, NJView Profile Authors Info & Claims ACM SIGIR ForumVolume 29Issue 2Fall 1995 pp 13–19https://doi.org/10.1145/219587.219592Published:01 September 1995Publication History 69citation995DownloadsMetricsTotal Citations69Total Downloads995Last 12 Months437Last 6 weeks74 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
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
Cited by
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
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
Replay in Deep Learning: Current Approaches and Missing Biological Elements
Hayes, T. L., Krishnan, G. P., Bazhenov, M., Siegelmann, H. T., Sejnowski, T. J., & Kanan, C. (2021) · Neural Computation · doi:10.1162/neco_a_01433 · Cited by 106
Optimization of formulations using robotic experiments driven by machine learning DoE
Cao, L., Russo, D., Felton, K., Salley, D., Sharma, A., Keenan, G., Mauer, W., Gao, H., Cronin, L., & Lapkin, A. A. (2021) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2020.100295 · Cited by 75
Active and semi-supervised learning for object detection with imperfect data
Rhee, P. K., Erdenee, E., Kyun, S. D., Ahmed, M. U., & Jin, S. (2017) · Cognitive Systems Research · doi:10.1016/j.cogsys.2017.05.006 · Cited by 55
Metadata
- Authors
- Lewis, D. D.
- Venue
- ACM SIGIR Forum
- Published
- 1995-09
- Volume
- 29
- Issue
- 2
- Pages
- 13-19
- ISSN
- 0163-5840 (ISSN-L)
- OpenAlex
- W2065010255
- Cited by
- 400
- References
- 0
- FWCI
- 3.35
- Type
- journal article
- Language
- en
- Open access
- closed
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: A sequential algorithm for training text classifiers: corrigendum and additional data. https://app.cochat.ai/papers/10.1145/219587.219592
- The paper
- Lewis, D. D. (1995). A sequential algorithm for training text classifiers: corrigendum and additional data. ACM SIGIR Forum. https://doi.org/10.1145/219587.219592
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.
