  An earlier introduced characterization of nonuniform learnability that allows
the sample size to depend on the hypothesis to which the learner is compared
has been redefined using the measure theoretic approach. Where nonuniform
learnability is a strict relaxation of the Probably Approximately Correct
framework. Introduction of a new algorithm, Generalize Measure Learnability
framework, to implement this approach with the study of its sample and
computational complexity bounds. Like the Minimum Description Length principle,
this approach can be regarded as an explication of Occam razor. Furthermore,
many situations were presented, Hypothesis Classes that are countable where we
can apply the GML framework, which we can learn to use the GML scheme and can
achieve statistical consistency.
