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We consider a declarative framework for machine learning where concepts andhypotheses are defined by formulas of a logic over some background structure.We show that within this framework, concepts defined by first-order formulasover a background structure of at most polylogarithmic degree can be learned inpolylogarithmic time in the "probably approximately correct" learning sense.

Learning first-order definable concepts over structures of small degree