Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting Regional Classification of Levantine Ivory Sculptures: A
  Machine Learning Approach   Art historians and archaeologists have long grappled with the regional
classification of ancient Near Eastern ivory carvings. Based on the visual
similarity of sculptures, individuals within these fields have proposed object
assemblages linked to hypothesized regional production centers. Using
quantitative rather than visual methods, we here approach this classification
task by exploiting computational methods from machine learning currently used
with success in a variety of statistical problems in science and engineering.
We first construct a prediction function using 66 categorical features as
inputs and regional style as output. The model assigns regional style group
(RSG), with 98 percent prediction accuracy. We then rank these features by
their mutual information with RSG, quantifying single-feature predictive power.
Using the highest- ranking features in combination with nomographic
visualization, we have found previously unknown relationships that may aid in
the regional classification of these ivories and their interpretation in art
historical context.

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The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
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The categories of this paper are: supr-con
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: math
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-ex
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The categories of this paper are: physics
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The categories of this paper are: math-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-alg