  This work establishes a new upper bound on the number of samples sufficient
for PAC learning in the realizable case. The bound matches known lower bounds
up to numerical constant factors. This solves a long-standing open problem on
the sample complexity of PAC learning. The technique and analysis build on a
recent breakthrough by Hans Simon.
