Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-40935-6_9
Title: Partial learning of recursively enumerable languages
Authors: Gao, Z.
Stephan, F. 
Zilles, S.
Issue Date: 2013
Citation: Gao, Z.,Stephan, F.,Zilles, S. (2013). Partial learning of recursively enumerable languages. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8139 LNAI : 113-127. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-40935-6_9
Abstract: This paper studies several typical learning criteria in the model of partial learning of r.e. sets in the recursion-theoretic framework of inductive inference. Its main contribution is a complete picture of how the criteria of confidence, consistency and conservativeness in partial learning of r.e. sets separate, also in relation to basic criteria of learning in the limit. Thus this paper constitutes a substantial extension to prior work on partial learning. Further highlights of this work are very fruitful characterisations of some of the inference criteria studied, leading to interesting consequences about the structural properties of the collection of classes learnable under these criteria. In particular a class is consistently partially learnable iff it is a subclass of a uniformly recursive family. © 2013 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/10635/104607
ISBN: 9783642409349
ISSN: 03029743
DOI: 10.1007/978-3-642-40935-6_9
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