Training, validation, and test data sets (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Training, validation, and test data sets" in English language version.

refsWebsite

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5doi.org

2^nd place

4nih.gov

4^th place

3worldcat.org

5^th place

2archive.org

6^th place

1stanford.edu

179^th place

183^rd place

1usc.edu

1,077^th place

761^st place

1machinelearningmastery.com

low place

1stackoverflow.com

4,643^rd place

2,973^rd place

1web.archive.org

1^st place

1coursera.org

low place

1books.google.com

3^rd place

1researchgate.net

120^th place

125^th place

1wired.com

193^rd place

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archive.org (Global: 6^th place; English: 6^th place)

Ripley, Brian (1996). Pattern Recognition and Neural Networks. Cambridge University Press. p. 354. ISBN 978-0521717700.
Prechelt, Lutz; Geneviève B. Orr (2012-01-01). "Early Stopping — But When?". In Grégoire Montavon; Klaus-Robert Müller (eds.). Neural Networks: Tricks of the Trade. Lecture Notes in Computer Science. Springer Berlin Heidelberg. pp. 53–67. doi:10.1007/978-3-642-35289-8_5. ISBN 978-3-642-35289-8.

books.google.com (Global: 3^rd place; English: 3^rd place)

Bishop, C.M. (1995), Neural Networks for Pattern Recognition, Oxford: Oxford University Press, p. 372

coursera.org (Global: low place; English: low place)

"Deep Learning". Coursera. Retrieved 2021-05-18.

doi.org (Global: 2^nd place; English: 2^nd place)

Ron Kohavi; Foster Provost (1998). "Glossary of terms". Machine Learning. 30: 271–274. doi:10.1023/A:1007411609915.
Prechelt, Lutz; Geneviève B. Orr (2012-01-01). "Early Stopping — But When?". In Grégoire Montavon; Klaus-Robert Müller (eds.). Neural Networks: Tricks of the Trade. Lecture Notes in Computer Science. Springer Berlin Heidelberg. pp. 53–67. doi:10.1007/978-3-642-35289-8_5. ISBN 978-3-642-35289-8.
Larose, D. T.; Larose, C. D. (2014). Discovering knowledge in data : an introduction to data mining. Hoboken: Wiley. doi:10.1002/9781118874059. ISBN 978-0-470-90874-7. OCLC 869460667.
Xu, Yun; Goodacre, Royston (2018). "On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning". Journal of Analysis and Testing. 2 (3). Springer Science and Business Media LLC: 249–262. doi:10.1007/s41664-018-0068-2. ISSN 2096-241X. PMC 6373628. PMID 30842888.
Chanda SS, Banerjee DN (2022). "Omission and commission errors underlying AI failures". AI Soc. 39 (3): 1–24. doi:10.1007/s00146-022-01585-x. PMC 9669536. PMID 36415822.

machinelearningmastery.com (Global: low place; English: low place)

Brownlee, Jason (2017-07-13). "What is the Difference Between Test and Validation Datasets?". Retrieved 2017-10-12.

nih.gov (Global: 4^th place; English: 4^th place)

ncbi.nlm.nih.gov

Xu, Yun; Goodacre, Royston (2018). "On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning". Journal of Analysis and Testing. 2 (3). Springer Science and Business Media LLC: 249–262. doi:10.1007/s41664-018-0068-2. ISSN 2096-241X. PMC 6373628. PMID 30842888.
Chanda SS, Banerjee DN (2022). "Omission and commission errors underlying AI failures". AI Soc. 39 (3): 1–24. doi:10.1007/s00146-022-01585-x. PMC 9669536. PMID 36415822.

pubmed.ncbi.nlm.nih.gov

Xu, Yun; Goodacre, Royston (2018). "On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning". Journal of Analysis and Testing. 2 (3). Springer Science and Business Media LLC: 249–262. doi:10.1007/s41664-018-0068-2. ISSN 2096-241X. PMC 6373628. PMID 30842888.
Chanda SS, Banerjee DN (2022). "Omission and commission errors underlying AI failures". AI Soc. 39 (3): 1–24. doi:10.1007/s00146-022-01585-x. PMC 9669536. PMID 36415822.

researchgate.net (Global: 120^th place; English: 125^th place)

Kohavi, Ron (2001-03-03). "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection". 14. {{cite journal}}: Cite journal requires |journal= (help)

stackoverflow.com (Global: 4,643^rd place; English: 2,973^rd place)

"Machine learning - Is there a rule-of-thumb for how to divide a dataset into training and validation sets?". Stack Overflow. Retrieved 2021-08-12.

stanford.edu (Global: 179^th place; English: 183^rd place)

ai.stanford.edu

Ron Kohavi; Foster Provost (1998). "Glossary of terms". Machine Learning. 30: 271–274. doi:10.1023/A:1007411609915.

usc.edu (Global: 1,077^th place; English: 761^st place)

www-bcf.usc.edu

James, Gareth (2013). An Introduction to Statistical Learning: with Applications in R. Springer. p. 176. ISBN 978-1461471370.

web.archive.org (Global: 1^st place; English: 1^st place)

"Subject: What are the population, sample, training set, design set, validation set, and test set?", Neural Network FAQ, part 1 of 7: Introduction (txt), comp.ai.neural-nets, Sarle, W.S., ed. (1997, last modified 2002-05-17)

wired.com (Global: 193^rd place; English: 152^nd place)

Greenberg A (2017-11-14). "Watch a 10-Year-Old's Face Unlock His Mom's iPhone X". Wired.

worldcat.org (Global: 5^th place; English: 5^th place)

search.worldcat.org

Larose, D. T.; Larose, C. D. (2014). Discovering knowledge in data : an introduction to data mining. Hoboken: Wiley. doi:10.1002/9781118874059. ISBN 978-0-470-90874-7. OCLC 869460667.
Xu, Yun; Goodacre, Royston (2018). "On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning". Journal of Analysis and Testing. 2 (3). Springer Science and Business Media LLC: 249–262. doi:10.1007/s41664-018-0068-2. ISSN 2096-241X. PMC 6373628. PMID 30842888.
Ripley, Brian D. (2008-01-10). "Glossary". Pattern recognition and neural networks. Cambridge University Press. ISBN 9780521717700. OCLC 601063414.

Training, validation, and test data sets (English Wikipedia)

archive.org (Global: 6th place; English: 6th place)

books.google.com (Global: 3rd place; English: 3rd place)

coursera.org (Global: low place; English: low place)

doi.org (Global: 2nd place; English: 2nd place)

machinelearningmastery.com (Global: low place; English: low place)

nih.gov (Global: 4th place; English: 4th place)

ncbi.nlm.nih.gov

pubmed.ncbi.nlm.nih.gov

researchgate.net (Global: 120th place; English: 125th place)

stackoverflow.com (Global: 4,643rd place; English: 2,973rd place)

stanford.edu (Global: 179th place; English: 183rd place)