Prescribed Learning of Indexed Families

Probabilistic learning of indexed families under monotonicity constraints: hierarchy results and complexity aspects

We are concerned with probabilistic identification of indexed families of uniformly recursive languages from positive data under monotonicity constraints. Thereby, we consider conservative, strong-monotonic and monotonic probabilistic learning of indexed families with respect to class comprising, class preserving and proper hypothesis spaces, and investigate the probabilistic hierarchies in the...

Learning Indexed Families of Recursive Languages from Positive Data

In the past 40 years, research on inductive inference has developed along different lines, concerning different formalizations of learning models and in particular of target concepts for learning. One common root of many of these is Gold’s model of identification in the limit. This model has been studied for learning recursive functions, recursively enumerable languages, and recursive languages...

Learning indexed families of recursive languages from positive data: A survey

In the past 40 years, research on inductive inference has developed along different lines, e.g., in the formalizations used, and in the classes of target concepts considered. One common root of many of these formalizations is Gold’s model of identification in the limit. This model has been studied for learning recursive functions, recursively enumerable languages, and recursive languages, refle...

Reeective Language Learning of Indexed Families

Learning with Refutation

In their pioneering work, Mukouchi and Arikawa modeled a learning situation in which the learner is expected to refute texts which are not representative of L, the class of languages being identified. Lange and Watson extended this model to consider justified refutation in which the learner is expected to refute texts only if it contains a finite sample unrepresentative of the class L. Both the...