The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Training algorithms for linear text classifiers
Open accessAbstract
Systems for text retrieval, routing, categorization and other IR tasks rely heavily on linear classifiers.We propose that two machine learning algorithms, the Widrow-Hoff and EG algorithms, be used in training linear text classifiers.In contrast to most IR methods, theoretical analysis provides performance guarantees and guidance on parameter settings for these algorithms.Experimental data is presented showing Widrow-Hoff and EG to be more effective than the widely used Rocchio algorithm on several categorization and routing tasks.
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
A Sequential Algorithm for Training Text Classifiers
Lewis, D. D., & Gale, W. A. (1994) · SIGIR ’94 · doi:10.1007/978-1-4471-2099-5_1 · Cited by 1,208
Self-citation
Cited by
Content-Based Recommendation Systems
Pazzani, M. J., & Billsus, D. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-72079-9_10 · Cited by 2,600
Androutsopoulos, I., Koutsias, J., Chandrinos, K. V., & Spyropoulos, C. D. (2000) · Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/345508.345569 · Cited by 465
Demographic prediction based on user's browsing behavior
Hu, J., Zeng, H.-J., Li, H., Niu, C., & Chen, Z. (2007) · Proceedings of the 16th international conference on World Wide Web · doi:10.1145/1242572.1242594 · Cited by 259
Automatically Identifying Health Outcome Information in MEDLINE Records
Demner-Fushman, D., Few, B., Hauser, S. E., & Thoma, G. (2006) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m1911 · Cited by 35
Devine, R. T., Kovatchev, V., Grumley Traynor, I., Smith, P., & Lee, M. (2023) · Psychological Assessment · doi:10.1037/pas0001186 · Cited by 12
Metadata
- Authors
- Lewis, D. D., Schapire, R. E., Callan, J. P., & Papka, R.
- Venue
- Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '96
- Publisher
- ACM Press
- Published
- 1996
- Pages
- 298-306
- OpenAlex
- W2060216474
- Cited by
- 520
- References
- 1
- FWCI
- 20.25
- Type
- conference paper
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Training algorithms for linear text classifiers. https://app.cochat.ai/papers/10.1145/243199.243277
- The paper
- Lewis, D. D., Schapire, R. E., Callan, J. P., & Papka, R. (1996). Training algorithms for linear text classifiers. Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '96. https://doi.org/10.1145/243199.243277
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.
