The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
Overview
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Integrity signals
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Claims the paper makes
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Self-check
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The self-check needs the paper’s full text.
Citations
Cites
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Valiant, L. G. (1984) · Communications of the ACM · doi:10.1145/1968.1972 · Cited by 3,279
Cited by
(1998) · Cited by 3,476
Authentic facial expression analysis
Sebe, N., Lew, M. S., Sun, Y., Cohen, I., Gevers, T., & Huang, T. S. (2007) · Image and Vision Computing · doi:10.1016/j.imavis.2005.12.021 · Cited by 257
Metadata
- Authors
- Aha, D. W.
- Venue
- International Journal of Man-Machine Studies
- Publisher
- Elsevier BV
- Published
- 1992-02
- Volume
- 36
- Issue
- 2
- Pages
- 267-287
- ISSN
- 0020-7373 (ISSN-L)
- OpenAlex
- W2067098334
- Cited by
- 405
- References
- 2
- FWCI
- 4.02
- 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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms. https://app.cochat.ai/papers/10.1016/0020-7373(92)90018-g
- The paper
- Aha, D. W. (1992). Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms. International Journal of Man-Machine Studies. https://doi.org/10.1016/0020-7373(92)90018-g
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.
