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Applications Of Machine Learning To Classification And Analysis Of Southwestern US Ceramic Designs

Author(s): Leszek Pawlowicz ; Christopher Downum ; Michael Terlep

Year: 2017

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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.


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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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min long: -115.532; min lat: 30.676 ; max long: -102.349; max lat: 42.033 ;

Record Identifiers

Abstract Id(s): 16435

Arizona State University The Andrew W. Mellon Foundation National Science Foundation National Endowment for the Humanities Society for American Archaeology Archaeological Institute of America