Learning to extrapolate using continued fractions: Predicting the critical temperature of superconductor materials

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@Misc{Moscato:2020:superconductor,

• author = "Pablo Moscato and Mohammad Nazmul Haque and Kevin Huang and Julia Sloan and Jon C. {de Oliveira}",
• title = "Learning to extrapolate using continued fractions: Predicting the critical temperature of superconductor materials",
• howpublished = "arXiv",
• year = "2020",
• month = "8 " # nov,
• 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",
• URL = "https://arxiv.org/abs/2012.03774",
• DOI = "doi:10.48550/ARXIV.2012.03774",
• size = "17 pages",
• 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