Learning first-order definable concepts over structures of small degree

@article{Grohe2017LearningFD,
  title={Learning first-order definable concepts over structures of small degree},
  author={Martin Grohe and Martin Ritzert},
  journal={2017 32nd Annual ACM/IEEE Symposium on Logic in Computer Science (LICS)},
  year={2017},
  pages={1-12}
}
We consider a declarative framework for machine learning where concepts and hypotheses are defined by formulas of a logic over some “background structure”. We show that within this framework, concepts defined by first-order formulas over a background structure of at most polylogarithmic degree can be learned in polylogarithmic time in the “probably approximately correct” learning sense. 
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