Learning first-order definable concepts over structures of small degree
Martin Grohe, Martin Ritzert · arXiv (Cornell University) · 2017
We consider a declarative framework for machine learning where concepts and hypotheses are defined by formulas of a logic over some 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.