This makes the theory suitable for domains where observations are (relatively) noise-free but not random, such as language learning[1] and automated scientific discovery.
This is a non-probabilistic version of statistical consistency, which also requires convergence to a correct model in the limit, but allows a learner to fail on data sequences with probability measure 0 [citation needed].
Other frameworks consider a much more restricted class of learning algorithms than Turing machines, for example, learners that compute hypotheses more quickly, for instance in polynomial time.
[citation needed] A particular learner is said to be able to "learn a language in the limit" if there is a certain number of steps beyond which its hypothesis no longer changes.
[clarification needed] This is done by the learner testing all possible Turing machine programs in turn until one is found which is correct so far - this forms the hypothesis for the current step.
It does not allow for limits of runtime or computer memory which can occur in practice, and the enumeration method may fail if there are errors in the input.
[7] Kevin Kelly has suggested that minimizing mind changes is closely related to choosing maximally simple hypotheses in the sense of Occam’s Razor.