Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.tcs.2013.04.009
Title: Mind change speed-up for learning languages from positive data
Authors: Jain, S. 
Kinber, E.
Keywords: Algorithmic and automatic learning
Inductive Inference
Mind changes
Speedup
Issue Date: 6-Oct-2013
Citation: Jain, S., Kinber, E. (2013-10-06). Mind change speed-up for learning languages from positive data. Theoretical Computer Science 489-490 : 37-47. ScholarBank@NUS Repository. https://doi.org/10.1016/j.tcs.2013.04.009
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 FM(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 FM(n) can be made smaller than any recursive unbounded non-decreasing function. We also establish how FM(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. © 2013 Elsevier B.V. All rights reserved.
Source Title: Theoretical Computer Science
URI: http://scholarbank.nus.edu.sg/handle/10635/77886
ISSN: 03043975
DOI: 10.1016/j.tcs.2013.04.009
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