The DOI matches its Crossref record.
The elements of statistical learning: Data mining, inference, and prediction
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
“Furthermore, this model was validated using 1000 permutation tests [27].”
Citations
Cited by
Sorted by citations
Membership Inference Attacks Against Machine Learning Models
Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754
Feature Selection: A Data Perspective
Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2018) · ACM Computing Surveys · doi:10.1145/3136625 · Cited by 2,442
Deep learning for computational biology
Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156651 · Cited by 1,473
“If the data set is small, k-fold cross-validation or bootstrapping can be used instead (Hastie et al , 2005).”
InfluentialA Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988293 · Cited by 1,392
Machine learning with big data: challenges and approaches.
L'Heureux, A., Grolinger, K., Elyamany, H. F., & Capretz, M. A. M. (2017) · IEEE Access · doi:10.1109/access.2017.2696365 · Cited by 1,023
“Regularization refers to techniques that aim to improve generalization and reduce overfitting; examples of regularization techniques include early stopping, Lasso, and Ridge [46].”
Metadata
- Authors
- Franklin, J.
- Affiliations
- UNSW Sydney (AU)
- Corresponding author
- Franklin, James
- ORCID
- Franklin, James
- Venue
- The Mathematical Intelligencer
- Published
- 2005-03
- Volume
- 27
- Issue
- 2
- Pages
- 83-85
- ISSN
- 0343-6993 (ISSN-L)
- OpenAlex
- W3215186461
- Cited by
- 1,400
- References
- 0
- Citations by year
- 2026: 104 · 2025: 126 · 2024: 58 · 2023: 74 · 2022: 100 · 2021: 101 · 2020: 194 · 2019: 92 · 2018: 60 · 2017: 79 · 2016: 137 · 2015: 72 · 2014: 54 · 2013: 42 · 2012: 21
- Type
- book review
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- The Mathematical Intelligencer (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- statistical learning, data mining, statistical inference, prediction
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 ›
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