Learning and extending sublanguages
A number of natural models for learning in the limit are introduced to deal with the situation when a learner is required to provide a grammar covering the input even if only a part of the target language is available. Examples of language families are exhibited that are learnable in one model and not learnable in another one. Some characterizations for learnability of algorithmically enumerable families of languages for the models in question are obtained. Since learnability of any part of the target language does not imply monotonicity of the learning process, we consider our models also under the additional monotonicity constraint. (C) 2008 Elsevier B.V. All rights reserved.
inductive inference learning sublanguages