A REVIEW OF SUPERVISED AND UNSUPERVISED PATTERN RECOGNITION IN ARCHAEOMETRY.
Principal component, cluster and discriminant analysis are multivariate statistical methods that are widely used in archaeometry. They are examples of what are known in some literatures as unsupervised and supervised learning methods. Over the past 20 years or so, a wide variety of other learning me...
| Publicado en: | Archaeometry Vol. 48; no. 4; pp. 671 - 695 |
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| Formato: | Artículo |
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Wiley-Blackwell
Nov2006
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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=22707065&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 22707065 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 0003813X D7X jtl: Archaeometry issn: 0003813X maglogo: Y pubinfo: dt: Nov2006 vid: 48 iid: 4 pid: 480 pub: Wiley-Blackwell artinfo: ui: 22707065 10.1111/j.1475-4754.2006.00280.x ppf: 671 ppct: 24 formats: tig: atl: A REVIEW OF SUPERVISED AND UNSUPERVISED PATTERN RECOGNITION IN ARCHAEOMETRY. aug: au: Baxter, M. J. affil: Division of Physics, School of Biomedical and Natural Sciences, Nottingham Trent University, Clifton Campus, Nottingham NG11 8NS, UK Division of Mathematical Sciences, School of Biomedical and Natural Sciences, Nottingham Trent University, Clifton Campus, Nottingham NG11 8NS, UK su: Archaeological surveying Pattern perception Supervised study Principal components analysis Cluster analysis (Statistics) Discriminant analysis Glass industry Logistic regression analysis sug: subj: Archaeological surveying Pattern perception Supervised study Principal components analysis Cluster analysis (Statistics) Discriminant analysis Glass industry Logistic regression analysis keyword: compositional data glass multivariate supervised learning unsupervised learning ab: Principal component, cluster and discriminant analysis are multivariate statistical methods that are widely used in archaeometry. They are examples of what are known in some literatures as unsupervised and supervised learning methods. Over the past 20 years or so, a wide variety of other learning methods have been developed that take advantage of modern computing power and, in some cases, have been designed to handle data sets more complex than those often used in archaeometric data analysis. To date, these methods have had little impact on archaeometry. This paper reviews, in a largely non-technical manner, the ideas behind these newer methods; illustrates their use on a variety of data sets; and attempts to assess their potential for future archaeometric use. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2006 holdings: @attributes: islocal: N |
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