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|Title:||Learning and classifying||Authors:||Jain, S.
|Issue Date:||2011||Citation:||Jain, S.,Martin, E.,Stephan, F. (2011). Learning and classifying. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6925 LNAI : 70-83. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-24412-4_9||Abstract:||We define and study a learning paradigm that sits between identification in the limit and classification. More precisely, we expect that a learner be able to identify in the limit which members of a set D of n possible data belong to a target language, where n and D are arbitrary. We show that Ex- and BC-learning are often more difficult than performing this classification task, taking into account desirable constraints on how the learner behaves, such as bounding the number of mind changes and being conservative. Special attention is given to various forms of consistency. We provide a fairly comprehensive set of results that demonstrate the fruitfulness of the approach and the richness of the paradigm. © 2011 Springer-Verlag.||Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||URI:||http://scholarbank.nus.edu.sg/handle/10635/41124||ISBN:||9783642244117||ISSN:||03029743||DOI:||10.1007/978-3-642-24412-4_9|
|Appears in Collections:||Staff Publications|
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