Please use this identifier to cite or link to this item:
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
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.
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
ISSN: 03043975
DOI: 10.1016/j.tcs.2013.04.009
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Jan 12, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.