Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-540-87987-9_35
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dc.titleNumberings optimal for learning
dc.contributor.authorJain, S.
dc.contributor.authorStephan, F.
dc.date.accessioned2013-07-23T09:30:05Z
dc.date.available2013-07-23T09:30:05Z
dc.date.issued2008
dc.identifier.citationJain, S.,Stephan, F. (2008). Numberings optimal for learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5254 LNAI : 434-448. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-540-87987-9_35" target="_blank">https://doi.org/10.1007/978-3-540-87987-9_35</a>
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/43290
dc.description.abstractThis paper extends previous studies on learnability in non-acceptable numberings by considering the question: for which criteria which numberings are optimal, that is, for which numberings it holds that one can learn every learnable class using the given numbering as hypothesis space. Furthermore an effective version of optimality is studied as well. It is shown that the effectively optimal numberings for finite learning are just the acceptable numberings. In contrast to this, there are non-acceptable numberings [3] which are optimal for finite learning and effectively optimal for explanatory, vacillatory and behaviourally correct learning. The numberings effectively optimal for explanatory learning are the K-acceptable numberings. A similar characterization is obtained for the numberings which are effectively optimal for vacillatory learning. Furthermore, it is studied which numberings are optimal for one and not for another criterion: among the criteria of finite, explanatory, vacillatory and behaviourally correct learning all separations can be obtained; however every numbering which is optimal for explanatory learning is also optimal for consistent learning. © 2008 Springer-Verlag Berlin Heidelberg.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-540-87987-9_35
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.contributor.departmentMATHEMATICS
dc.description.doi10.1007/978-3-540-87987-9_35
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume5254 LNAI
dc.description.page434-448
dc.identifier.isiutNOT_IN_WOS
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