The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Multiple surrogates: how cross-validation errors can help us to obtain the best predictor
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
Neural networks for pattern recognition
(1994) · Choice Reviews Online · doi:10.5860/choice.31-5500 · Cited by 18,538
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,387
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909
Response Surface Methodology: Process and Product Optimization Using Designed Experiments
Gunst, R. F., Myers, R. H., & Montgomery, D. C. (1996) · Technometrics · doi:10.2307/1270613 · Cited by 9,530
McKay, M. D., Beckman, R. J., & Conover, W. J. (2000) · Technometrics · doi:10.2307/1271432 · Cited by 7,415
Design and Analysis of Computer Experiments
Sacks, J., Welch, W. J., Mitchell, T. J., & Wynn, H. P. (1989) · Statistical Science · doi:10.1214/ss/1177012413 · Cited by 6,919
Addelman, S. (1979) · Technometrics · doi:10.1080/00401706.1979.10489788 · Cited by 6,750
McKay, M. D., Beckman, R. J., & Conover, W. J. (1979) · Technometrics · doi:10.2307/1268522 · Cited by 3,757
Response Surface Methodology: Process and Product Optimization Using Designed Experiments
Gunst, R. F. (1996) · Technometrics · doi:10.1080/00401706.1996.10484509 · Cited by 3,255
Cited by
Yondo, R., Andrés, E., & Valero, E. (2018) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2017.11.003 · Cited by 311
Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis
Nascimento, R. G., Corbetta, M., Kulkarni, C. S., & Viana, F. A. C. (2021) · Journal of Power Sources · doi:10.1016/j.jpowsour.2021.230526 · Cited by 224
Self-citation
Metadata
- Authors
- Viana, F. A. C., Haftka, R. T., & Steffen, V.
- Venue
- Structural and Multidisciplinary Optimization
- Published
- 2009-01-08
- Volume
- 39
- Issue
- 4
- Pages
- 439-457
- ISSN
- 1615-147X (ISSN-L)
- 1615-1488
- OpenAlex
- W2105782540
- Cited by
- 420
- References
- 21
- FWCI
- 17.06
- 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.
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: Multiple surrogates: how cross-validation errors can help us to obtain the best predictor. https://app.cochat.ai/papers/10.1007/s00158-008-0338-0
- The paper
- Viana, F. A. C., Haftka, R. T., & Steffen, V. (2009). Multiple surrogates: how cross-validation errors can help us to obtain the best predictor. Structural and Multidisciplinary Optimization. https://doi.org/10.1007/s00158-008-0338-0
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.
