AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
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STUDIA INFORMATICA - Ediţia nr.2 din 2015 | |||||||
Articol: |
A NEW UNSUPERVISED LEARNING BASED APPROACH FOR GENDER DETECTION OF HUMAN ARCHAEOLOGICAL REMAINS. Autori: . |
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Rezumat:
Detecting the gender of human skeletal remains is an important problem within archaeology, since it is essential for understanding the characteristics of past societies. We approach in this paper, from a machine learning perspective, the problem of sex identication of human skeletal remains from bone measurements. In order to partition a group of skeleton remains according to their gender, different clustering algorithms are considered. Computational experiments carried out on publicly available archaeological data sets show a good performance of the proposed clustering approaches with respect to existing similar approaches from the literature. 2010 Mathematics Subject Classification. 68T05,62H30. Key words and phrases. bioarchaeology, sex determination, machine learning, clustering.
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