Measure Theoretic Approach to Nonuniform Learnability
This work addresses theoretical machine learning foundations, offering a new framework for nonuniform learnability, though it appears incremental as it builds on prior characterizations.
The paper tackles the problem of nonuniform learnability by redefining it using a measure theoretic approach and introducing the Generalize Measure Learnability (GML) framework. It presents sample and computational complexity bounds for GML and shows it can achieve statistical consistency for countable hypothesis classes.
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.