Machine Learning Techniques For Analyzing Inscriptions From Israel.
The date of artifacts is an important factor for scholars to get a further understanding of culture and society of the past. However, many artifacts are damaged over time, and we can often only get fragments of information regarding the original artifact. Here, we use the inscription data from Israe...
| Publicado en: | DHQ: Digital Humanities Quarterly Vol. 17; no. 3; pp. 1 - 13 |
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| Autores principales: | , |
| Formato: | Artículo |
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Digital Humanities Quarterly
2023
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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=173348894&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 173348894 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 19384122 77X0 jtl: DHQ: Digital Humanities Quarterly issn: 19384122 maglogo: N pubinfo: dt: 2023 vid: 17 iid: 3 pid: 47240 pub: Digital Humanities Quarterly artinfo: ui: 173348894 ppf: 1 ppct: 12 formats: tig: atl: Machine Learning Techniques For Analyzing Inscriptions From Israel. aug: au: Tagami, Daiki Satlow, Michael affil: Columbia University Brown University su: Inscriptions Random forest algorithms Digital humanities Machine learning Israel Palestine sug: subj: Israel Palestine Inscriptions Random forest algorithms Digital humanities Machine learning ab: The date of artifacts is an important factor for scholars to get a further understanding of culture and society of the past. However, many artifacts are damaged over time, and we can often only get fragments of information regarding the original artifact. Here, we use the inscription data from Israel as a model dataset and compare the performances of eleven commonly used regression models. We find that the random forest model would be the optimal machine learning model to predict the year of inscriptions from tabular data. We further show how we can make interpretations from the machine learning prediction model through a variance important plot. This research shows an overview of how machine learning techniques could be used to resolve digital humanities problems by using the Inscription of Israel/Palestine dataset as a model dataset A comparison of machine learning models to predict the year of inscriptions from tabular data in the Israel/Palestine dataset. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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