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Unsupervised deep learning supports reclassification of Bronze age cypriot writing system
Michele Corazza, Fabio Tamburini, Miguel Valério et al., 2022. PLOS ONE.
Uses deep learning on sign shapes, together with paleographic and epigraphic evidence, to test the usual division of the undeciphered Cypro-Minoan script into three subgroups, and finds support for reclassifying it.
What it's used for here
The inductive-methods survey cites its unsupervised grouping of signs as a model for clustering Voynich glyph images.
Cited by on this site 2 records
2 methods surveys
SRCMathematics, inductive methods
Cite it
Corazza, M., Tamburini, F., Valério, M., & Ferrara, S. (2022). Unsupervised deep learning supports reclassification of Bronze age cypriot writing system. PLOS ONE. https://doi.org/10.1371/journal.pone.0269544
@article{corazza2022unsupervised,
author = {Corazza, Michele and Tamburini, Fabio and Valério, Miguel and Ferrara, Silvia},
title = {Unsupervised deep learning supports reclassification of Bronze age cypriot writing system},
journal = {PLOS ONE},
year = {2022},
doi = {10.1371/journal.pone.0269544},
url = {https://doi.org/10.1371/journal.pone.0269544}
}TY - JOUR TI - Unsupervised deep learning supports reclassification of Bronze age cypriot writing system AU - Corazza, Michele AU - Tamburini, Fabio AU - Valério, Miguel AU - Ferrara, Silvia PY - 2022 JO - PLOS ONE DO - 10.1371/journal.pone.0269544 UR - https://doi.org/10.1371/journal.pone.0269544 ER -
Corazza, Michele et al.. “Unsupervised deep learning supports reclassification of Bronze age cypriot writing system.” PLOS ONE (2022). https://doi.org/10.1371/journal.pone.0269544.