In this framework the learner get samples that are classified according to a function from a certain class. The aim of the learner is to find an approximation of the function with high probability. We demand the learner to be able to learn the concept given any arbitrary approximation ratio, probability of success or distribution of the samples.
The model was further extended to treat noise (misclassified samples). The PAC framework allowed accurate mathematical analysis of learning.
The original theory was published at L. Valiant. A theory of the learnable. Communications of the ACM, 27, 1984.
PAC learning framework is part of computational learning theory .