  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.
