Finding True Clusters: On the Importance of Simplicity in Science.
The main point of this paper is to underscore the link between simplicity and truth in an unsupervised machine learning context. More precisely, we argue that parametric and dimensional simplicity are not indicators of truth but the methodological principle that urges us to pay attention to such not...
| Publicado en: | Erkenntnis Vol. 87; no. 5; pp. 2081 - 2097 |
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| Autores principales: | , |
| Formato: | Artículo |
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Springer Nature
Oct2022
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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=159159890&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 159159890 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 01650106 5KZ jtl: Erkenntnis issn: 01650106 maglogo: N pubinfo: dt: Oct2022 vid: 87 iid: 5 pid: 237 pub: Springer Nature artinfo: ui: 159159890 10.1007/s10670-020-00291-8 ppf: 2081 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.9MB tig: atl: Finding True Clusters: On the Importance of Simplicity in Science. aug: au: Rochefort-Maranda, Guillaume Liu, Mo affil: Laval University, Quebec, Canada Lecturer School of Marxism, Shanghai University of Engineering Science, No. 333 Longteng Road, Songjiang District, 201620, Shanghai, People's Republic of China su: Simplicity Machine learning sug: subj: Simplicity Machine learning ab: The main point of this paper is to underscore the link between simplicity and truth in an unsupervised machine learning context. More precisely, we argue that parametric and dimensional simplicity are not indicators of truth but the methodological principle that urges us to pay attention to such notions of simplicity is truth conducive. The truth that we are looking for are specific geometrical shapes and we know which algorithm can find which shapes provided that we pay attention to parametric and dimensional simplicity. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Erkenntnis is a copyright of Springer, 2022. All Rights Reserved. item: Erkenntnis holder: Springer Nature dt: @attributes: year: 2022 holdings: @attributes: islocal: N |
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