Given N learners each capable of learning concepts (subsets) in the se
nse of Valiant, we are interested in combining them using a single fus
er. We consider two cases. In open fusion the fuser is given the sampl
e and the hypotheses of the individual learners; we show that a fusion
rule can be obtained by formulating this problem as another learning
problem. We show sufficiency conditions that ensure the composite syst
em to be better than the best of the individual. Second, in closed fus
ion the fuser does not have an access to either the training sample or
the hypotheses of the individual learners. By using a linear threshol
d fusion function (of the outputs of individual learners) we show that
the composite system can be made better than the best of the statisti
cally independent learners.