Please use this identifier to cite or link to this item: https://doi.org/10.4230/LIPIcs.STACS.2012.350
Title: Mind change speed-up for learning languages from positive data
Authors: Jain, S. 
Kinber, E.
Keywords: Algorithmic and automatic learning
Mind changes
Speedup
Issue Date: 2012
Citation: Jain, S., Kinber, E. (2012). Mind change speed-up for learning languages from positive data. Leibniz International Proceedings in Informatics, LIPIcs 14 : 350-361. ScholarBank@NUS Repository. https://doi.org/10.4230/LIPIcs.STACS.2012.350
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. © S. Jain and E. Kinber.
Source Title: Leibniz International Proceedings in Informatics, LIPIcs
URI: http://scholarbank.nus.edu.sg/handle/10635/78226
ISBN: 9783939897354
ISSN: 18688969
DOI: 10.4230/LIPIcs.STACS.2012.350
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