Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-04414-4_26
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dc.titleIterative learning from texts and counterexamples using additional information
dc.contributor.authorJain, S.
dc.contributor.authorKinber, E.
dc.date.accessioned2013-07-04T08:19:55Z
dc.date.available2013-07-04T08:19:55Z
dc.date.issued2009
dc.identifier.citationJain, S.,Kinber, E. (2009). Iterative learning from texts and counterexamples using additional information. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5809 LNAI : 308-322. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-04414-4_26" target="_blank">https://doi.org/10.1007/978-3-642-04414-4_26</a>
dc.identifier.isbn3642044131
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/41113
dc.description.abstractA variant of iterative learning in the limit (cf. [LZ96]) is studied when a learner gets negative examples refuting conjectures containing data in excess of the target language and uses additional information of the following four types: a) memorizing up to n input elements seen so far; b) up to n feedback memberships queries (testing if an item is a member of the input seen so far); c) the number of input elements seen so far; d) the maximal element of the input seen so far. We explore how additional information available to such learners (defined and studied in [JK07]) may help. In particular, we show that adding the maximal element or the number of elements seen so far helps such learners to infer any indexed class of languages class-preservingly (using a descriptive numbering defining the class) - as it is proved in [JK07], this is not possible without using additional information. We also study how, in the given context, different types of additional information fare against each other, and establish hierarchies of learners memorizing n + 1 versus n input elements seen and n + 1 versus n feedback membership queries. © 2009 Springer.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-04414-4_26
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1007/978-3-642-04414-4_26
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume5809 LNAI
dc.description.page308-322
dc.identifier.isiutNOT_IN_WOS
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