The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Fast instance selection for speeding up support vector machines
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“That is, the support vectors are mostly distributed in the boundary area, which are far away from the centroid point of each class.”
From An efficient instance selection algorithm to reconstruct training set for support vector machine (2017) · cites this paper as Chen [43]
In the citing paper
“As pointed out by Chen [43] that positive instances far away from centers of positive class and negative instances close to these centers are near the boundary.”
From An efficient instance selection algorithm to reconstruct training set for support vector machine (2017) · cites this paper as Chen [43]
In the citing paper
“However, this method is based on an impractical assumption of no possible class overlap.”
From An efficient instance selection algorithm to reconstruct training set for support vector machine (2017) · cites this paper as [43]
Citations
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
UCI Repository of machine learning databases
Blake, C. L. (1998) · Medical Entomology and Zoology · Cited by 10,582
Text categorization with Support Vector Machines: Learning with many relevant features
Joachims, T. (1998) · Lecture Notes in Computer Science · doi:10.1007/bfb0026683 · Cited by 7,999
A comparison of methods for multiclass support vector machines
Hsu, C.-W., & Lin, C.-J. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/72.991427 · Cited by 6,838
The Nature of Statistical Learning Theory
Sain, S. R. (1996) · Technometrics · doi:10.1080/00401706.1996.10484565 · Cited by 5,434
Training support vector machines: an application to face detection
Osuna, E., Freund, R., & Girosit, F. (2002) · Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1997.609310 · Cited by 2,486
Editorial: special issue on learning from imbalanced data sets
Chawla, N. V., Japkowicz, N., & Kotcz, A. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007733 · Cited by 2,063
Statistical Comparisons of Classifiers over Multiple Data Sets
DemšarJanez (2006) · Journal of Machine Learning Research · doi:10.5555/1248547.1248548 · Cited by 1,074
Successive overrelaxation for support vector machines
Mangasarian, O. L., & Musicant, D. R. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.788643 · Cited by 438
Research works printed but not linked: 1
- Koggalage, 2004, Reducing the number of training instances for fast support vector machine classification, Neural Information Processing Letters and Reviews, 2, 57
Cited by
Dong, R., Weng, S., Yang, L., & Liu, J. (2015) · Analytical Chemistry · doi:10.1021/acs.analchem.5b00137 · Cited by 195
An efficient instance selection algorithm to reconstruct training set for support vector machine
Liu, C., Wang, W., Wang, M., Lv, F., & Konan, M. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.10.031 · Cited by 146
Self-citation
Metadata
- Authors
- Chen, J., Zhang, C., Xue, X., & Liu, C.-L.
- Affiliations
- Shandong University of Finance and Economics (CN)
- Shandong University (CN)
- Tongji University (CN)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Chen, Jingnian
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2013-06
- Volume
- 45
- Pages
- 1-7
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W2041449151
- Cited by
- 53
- References
- 24
- Citations by year
- 2024: 2 · 2023: 4 · 2022: 2 · 2021: 3 · 2020: 6 · 2019: 6 · 2018: 2 · 2017: 8 · 2016: 9 · 2015: 6 · 2014: 4 · 2013: 1
- FWCI
- 7.37
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Knowledge-Based Systems (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Keywords
- instance selection, support vector machine, multi-class classification, cluster centers, training complexity reduction, binary classification, big data
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: Fast instance selection for speeding up support vector machines. https://app.cochat.ai/papers/10.1016/j.knosys.2013.01.031
- The paper
- Chen, J., Zhang, C., Xue, X., & Liu, C.-L. (2013). Fast instance selection for speeding up support vector machines. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2013.01.031
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.
