Supervised Machine Learning Algorithms: Classification and Comparison
Integrity signals
Registry
Cites
Statistics
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?
Supervised learning algorithms are trained by fully labelled datasets and can then make predictions on new data.
“Supervised learning algorithms are trained by fully labelled datasets [140], and can then make predictions on new data [141].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [141]
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works
Mullin, A., & Rosenblatt, F. F. (1963) · American Mathematical Monthly · doi:10.2307/2312103 · Cited by 2,223 · see citing works
Decision fusion using a multi-linear classifier
Verlinde, P. S. A., Maître, G., & Mayoraz, E. (1998) · Cited by 13 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Review of Wearable Devices and Data Collection Considerations for Connected Health
Vijayan, V., Connolly, J. P., Condell, J., McKelvey, N., & Gardiner, P. (2021) · Sensors · doi:10.3390/s21165589 · Cited by 435 · see citing works
Solano Meza, J. K., Orjuela Yepes, D., Rodrigo-Ilarri, J., & Rodrigo-Clavero, M.-E. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20054256 · Cited by 24 · see citing works
Park, J., Na, H. J., & Kim, H. (2024) · IEEE Access · doi:10.1109/access.2024.3519219 · Cited by 10 · see citing works
- Authors
- Babcock University, F.Y, O., J.E.T, A., O, A., J. O, H., O, O., & J, A.
- Venue
- International Journal of Computer Trends and Technology
- Published
- 2017-06-25
- Volume
- 48
- Issue
- 3
- Pages
- 128-138
- ISSN
- 2231-2803 (ISSN-L)
- OpenAlex
- W2756291343
- Cited by
- 932
- References
- 4
- Citations by year
- 2026: 56 · 2025: 152 · 2024: 196 · 2023: 198 · 2022: 151 · 2021: 107 · 2020: 49 · 2019: 16 · 2018: 6 · 2017: 1
- FWCI
- 36.72
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Computer Trends and Technology (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- supervised learning, machine learning classifiers, algorithm comparison
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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