Document Type

Article

Publication Date

6-2013

Abstract

Within the frameworks of learning in the limit of indexed classes of recursive languages from positive data and automatic learning in the limit of indexed classes of regular languages (with automatically computable sets of indices), we study the problem of minimizing the maximum number of mind changes by a learner on all languages with indices not exceeding . For inductive inference of recursive languages, we establish two conditions under which can be made smaller than any recursive unbounded non-decreasing function. We also establish how is affected if at least one of these two conditions does not hold. In the case of automatic learning, some partial results addressing speeding up the function are obtained.

Comments

Published: Jain, Sanjay, Kinber, Effim. "Mind Change Speed-Up for Learning Languages From Positive Data." Theoretical Computer Science 489-490 (2013): 37-47.

DOI

10.1016/j.tcs.2013.04.009


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