Semi-supervised machine learning approaches for predicting the chronology of archaeological sites: A case study of temples from medieval Angkor, Cambodia.
- DOI
- 10.1371/journal.pone.0205649
- Published
- 2018
- Container
- PloS one
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1371/journal.pone.0205649,
title = {Semi-supervised machine learning approaches for predicting the chronology of archaeological sites: A case study of temples from medieval Angkor, Cambodia.},
author = {Klassen S and Weed J and Evans D},
year = {2018},
journal = {PloS one},
doi = {10.1371/journal.pone.0205649},
url = {https://doi.org/10.1371/journal.pone.0205649}
}RIS
TY - JOUR TI - Semi-supervised machine learning approaches for predicting the chronology of archaeological sites: A case study of temples from medieval Angkor, Cambodia. AU - Klassen S AU - Weed J AU - Evans D PY - 2018 JO - PloS one DO - 10.1371/journal.pone.0205649 UR - https://doi.org/10.1371/journal.pone.0205649 ER -
APA
S, K., J, W., & D, E. (2018). Semi-supervised machine learning approaches for predicting the chronology of archaeological sites: A case study of temples from medieval Angkor, Cambodia.. PloS one. https://doi.org/10.1371/journal.pone.0205649
Source records
- pubmed · retrieved 2026-09-25T18:34:00.221Z