The Curve Fitting Problem, Data Validation, and Inductive Generalization in Machine Learning.
Aris Spanos and Deborah Mayo's error-statistical approach to statistical modeling and inference adopts the reliability of inductive inference as a primary criterion for statistical model and estimator selection (e.g., curve fitting). In this paper, we expand the error-statistical approach's adoption...
| Publicado en: | Erkenntnis Vol. 90; no. 8; pp. 3767 - 3782 |
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| Autores principales: | , |
| Formato: | Artículo |
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Springer Nature
Dec2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=190357311&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 190357311 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 01650106 5KZ jtl: Erkenntnis issn: 01650106 maglogo: N pubinfo: dt: Dec2025 vid: 90 iid: 8 pid: 237 pub: Springer Nature artinfo: ui: 190357311 10.1007/s10670-024-00863-y ppf: 3767 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P size: 954KB tig: atl: The Curve Fitting Problem, Data Validation, and Inductive Generalization in Machine Learning. aug: au: Tamir, Michael Shech, Elay affil: https://ror.org/05t99sp05 University of California, Berkeley, Berkeley, USA https://ror.org/02v80fc35 Auburn University, Auburn, USA su: Curve fitting Induction (Logic) Statistical errors Theory of knowledge Machine learning Data integrity Statistical models sug: subj: Curve fitting Induction (Logic) Statistical errors Theory of knowledge Machine learning Data integrity Statistical models keyword: Mathematical Sciences Statistics ab: Aris Spanos and Deborah Mayo's error-statistical approach to statistical modeling and inference adopts the reliability of inductive inference as a primary criterion for statistical model and estimator selection (e.g., curve fitting). In this paper, we expand the error-statistical approach's adoption of reliable inductive inference by scrutinizing the epistemic legitimacy of contemporary techniques leveraged in data science. We argue that data validation and testing potentially provides a direct, measurable method of evaluating evidence for reliable inductive inferences in cases where the error-statistical approach is not easily applied, and conclude with an exploration of core methodological foils to the reliability of inductive inference revealed by this argument. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Erkenntnis is a copyright of Springer, 2025. All Rights Reserved. item: Erkenntnis holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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