Learning to extrapolate using continued fractions: Predicting the critical temperature of superconductor materials
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Misc{Moscato:2020:superconductor,
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author = "Pablo Moscato and Mohammad Nazmul Haque and
Kevin Huang and Julia Sloan and Jon C. {de Oliveira}",
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title = "Learning to extrapolate using continued fractions:
Predicting the critical temperature of superconductor
materials",
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howpublished = "arXiv",
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year = "2020",
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month = "8 " # nov,
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keywords = "genetic algorithms, genetic programming, Machine
Learning (cs.LG), Superconductivity
(cond-mat.supr-con), Artificial Intelligence (cs.AI),
Neural and Evolutionary Computing (cs.NE), FOS:
Computer and information sciences, FOS: Computer and
information sciences, FOS: Physical sciences, FOS:
Physical sciences",
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URL = "https://arxiv.org/abs/2012.03774",
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DOI = "doi:10.48550/ARXIV.2012.03774",
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size = "17 pages",
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abstract = "n Artificial Intelligence we often seek to identify an
unknown target function of many variables y=f(x) giving
a limited set of instances S={(x(i),y(i))} with x(i) in
D where D is a domain of interest. We refer to S as the
training set and the final quest is to identify the
mathematical model that approximates this target
function for new x; with the set T={x(j)}
- }
Genetic Programming entries for
Pablo Moscato
Mohammad Nazmul Haque
Kevin Huang
Julia Sloan
Jon C de Oliveira
Citations