Applications Of Machine Learning To Classification And Analysis Of Southwestern US Ceramic Designs
Author(s): Leszek Pawlowicz; Christopher Downum; Michael Terlep
Year: 2017
Summary
Recent advances in hardware and software have made implementation of advanced machine learning algorithms for image classification and analysis faster and more accessible. We demonstrate the applicability of machine learning to the classification and analysis of common decorated ceramic types from Northern Arizona. Both supervised and unsupervised learning algorithms are used to investigate standard ceramic typologies, as well as design/temporal similarities/differences between different ceramic design types.
Cite this Record
Applications Of Machine Learning To Classification And Analysis Of Southwestern US Ceramic Designs. Leszek Pawlowicz, Christopher Downum, Michael Terlep. Presented at The 81st Annual Meeting of the Society for American Archaeology, Vancouver, British Columbia. 2017 ( tDAR id: 430587)
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Keywords
General
Machine Learning
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Southwestern Ceramics
Geographic Keywords
North America - Southwest
Spatial Coverage
min long: -115.532; min lat: 30.676 ; max long: -102.349; max lat: 42.033 ;
Record Identifiers
Abstract Id(s): 16435