A parametrization scheme for classifying models of learnability

We present a systematic framework for classifying, comparing and defining models of computational learnability. Apart from the obvious ‘uniformity’ parameters we present a novel ‘solid learnability’ notion that captures the difference between ‘Guess and Test’ learning algorithms and learnability notions for which consistency with the samples guarantees success.