The deep neural network approach to the reference class problem.
Methods of machine learning (ML) are gradually complementing and sometimes even replacing methods of classical statistics in science. This raises the question whether ML faces the same methodological problems as classical statistics. This paper sheds light on this question by investigating a long-st...
| Publicado en: | Synthese Vol. 201; no. 3; pp. 1 - 25 |
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| Autor principal: | |
| Formato: | Artículo |
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Springer Nature
Mar2023
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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=162490163&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 162490163 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Mar2023 vid: 201 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 162490163 10.1007/s11229-023-04110-9 ppf: 1 ppct: 24 formats: fmt: – @attributes: type: T – @attributes: type: P size: 557KB tig: atl: The deep neural network approach to the reference class problem. aug: au: Buchholz, Oliver affil: University of Tübingen, Cluster of Excellence “Machine Learning: New Perspectives for Science”, Maria-von-Linden-Str. 6, 72076, Tübingen, Germany sug: keyword: Big data Deep neural networks Machine learning Overfitting Prediction Reference class problem ab: Methods of machine learning (ML) are gradually complementing and sometimes even replacing methods of classical statistics in science. This raises the question whether ML faces the same methodological problems as classical statistics. This paper sheds light on this question by investigating a long-standing challenge to classical statistics: the reference class problem (RCP). It arises whenever statistical evidence is applied to an individual object, since the individual belongs to several reference classes and evidence might vary across them. Thus, the problem consists in choosing a suitable reference class for the individual. I argue that deep neural networks (DNNs) are able to overcome specific instantiations of the RCP. Whereas the criteria of narrowness, reliability, and homogeneity, that have been proposed to determine a suitable reference class, pose an inextricable tradeoff to classical statistics, DNNs are able to satisfy them in some situations. On the one hand, they can exploit the high dimensionality in big-data settings. I argue that this corresponds to the criteria of narrowness and reliability. On the other hand, ML research indicates that DNNs are generally not susceptible to overfitting. I argue that this property is related to a particular form of homogeneity. Taking both aspects together reveals that there are specific settings in which DNNs can overcome the RCP. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2023. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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