Synthesizing noise-tolerant language learners

An index for an r.e. class of languages (by definition) generates a sequenc e of grammars defining the class. An index for an indexed family of languag es (by definition) generates a sequence of decision procedures defining the family. F. Stephan's model of noisy data is employed, in which, roughly, c orrect data crops up infinitely often, and incorrect data only finitely oft en. Studied, then, is the synthesis from indices for r.e. classes and for i ndexed families of. languages of various kinds of noise-tolerant language-l earners for the corresponding classes or families indexed. Many positive re sults, as well as some negative results, are presented regarding the existe nce of such synthesizers. The proofs of most of the positive results yield, as pleasant corollaries, strict subset-principle or tell-tale style charac terizations for the noise-tolerant learn-ability of the corresponding class es or families indexed. (C) 2001 Elsevier Science B.V. All rights reserved.