Please use this identifier to cite or link to this item:
|Title:||Synthesizing noise-tolerant language learners|
|Citation:||Case, J., Jain, S., Sharma, A. (2001). Synthesizing noise-tolerant language learners. Theoretical Computer Science 261 (1) : 31-56. ScholarBank@NUS Repository. https://doi.org/10.1016/S0304-3975(00)00132-8|
|Abstract:||An index for an r.e. class of languages (by definition) generates a sequence of grammars defining the class. An index for an indexed family of languages (by definition) generates a sequence of decision procedures defining the family. F. Stephan's model of noisy data is employed, in which, roughly, correct data crops up infinitely often, and incorrect data only finitely often. Studied, then, is the synthesis from indices for r.e. classes and for indexed families of languages of various kinds of noise-tolerant language-learners for the corresponding classes or families indexed. Many positive results, as well as some negative results, are presented regarding the existence of such synthesizers. The proofs of most of the positive results yield, as pleasant corollaries, strict subset-principle or tell-tale style characterizations for the noise-tolerant learnability of the corresponding classes or families indexed. © 2001 Elsevier Science B.V. All rights reserved.|
|Source Title:||Theoretical Computer Science|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Sep 13, 2018
WEB OF SCIENCETM
checked on Sep 4, 2018
checked on Aug 17, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.