Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-24412-4_8
Title: Robust learning of automatic classes of languages
Authors: Jain, S. 
Martin, E.
Stephan, F. 
Keywords: inductive inference
learning in the limit
query learning
robust learning
translations
Issue Date: 2011
Source: Jain, S.,Martin, E.,Stephan, F. (2011). Robust learning of automatic classes of languages. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6925 LNAI : 55-69. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-24412-4_8
Abstract: This paper adapts and investigates the paradigm of robust learning, originally defined in the inductive inference literature for classes of recursive functions, to learning languages from positive data. Robustness is a very desirable property, as it captures a form of invariance of learnability under admissible transformations on the object of study. The classes of languages of interest are automatic - a formal concept that captures the notion of being recognisable by a finite automaton. A class of first-order definable operators - called translators - is introduced as natural transformations that preserve automaticity of languages in a given class and the inclusion relations between languages in the class. For many learning criteria, we characterise the classes of languages all of whose translations are learnable under that criterion. The learning criteria have been chosen from the literature on both explanatory learning from positive data and query learning, and include consistent and conservative learning, strong-monotonic learning, strong-monotonic consistent learning, finite learning, learning from subset queries, learning from superset queries, and learning from membership queries. © 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/10635/43281
ISBN: 9783642244117
ISSN: 03029743
DOI: 10.1007/978-3-642-24412-4_8
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