Abstract
The present work initiates the study of the learnability of automatic indexable classes which are classes of regular languages of a certain form. Angluin’s tell-tale condition characterizes when these classes are explanatorily learnable. Therefore, the more interesting question is when learnability holds for learners with complexity bounds, formulated in the automata-theoretic setting. The learners in question work iteratively, in some cases with an additional long-term memory, where the update function of the learner mapping old hypothesis, old memory and current datum to new hypothesis and new memory is automatic. Furthermore, the dependence of the learnability on the indexing is also investigated. This work brings together the fields of inductive inference and automatic structures.
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Jain, S., Luo, Q., Stephan, F. (2010). Learnability of Automatic Classes. In: Dediu, AH., Fernau, H., Martín-Vide, C. (eds) Language and Automata Theory and Applications. LATA 2010. Lecture Notes in Computer Science, vol 6031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13089-2_27
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DOI: https://doi.org/10.1007/978-3-642-13089-2_27
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