Mind change speed-up for learning languages from positive data
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 F-M(n) by a learner M on all languages with indices not exceeding n. For inductive inference of recursive languages, we establish two conditions under which F-M(n) can be made smaller than any recursive unbounded non-decreasing function. We also establish how F-M(n) 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 F-M(n) are obtained. (c) 2013 Elsevier B.V. All rights reserved.
mind changes inductive inference algorithmic and automatic learning speedup