Majority and location based fusers for systems of PAC concept learners

A system of probably and approximately correct learners of Valiant type that infer concepts from a sample is considered. Each learner had been trained by a sample using the methods of minimizing the empirical error, and no examples are available to the fuser. A majority fuser is known to make the composite system better than the best of the learners in terms of normalized confidence (that corresponds to the same precision value). An analysis of general majority fusers is carried out to obtain bounds on actual and expected errors. Conditions under which the r of N fuser performs better, in terms of normalized confidence or precision, than best of the individual learners are obtained. For a special class of statistically independent learners, slightly weaker conditions are obtained. Two fusers that use the location information of a test point are proposed, and are shown to be better than a learner with least empirical error