Abstract
Linearity and determinism seem to be two essential conditions for polynomial language learning to be possible. We compare several definitions of deterministic linear grammars, and for a reasonable definition prove the existence of a canonical normal form. This enables us to obtain positive learning results in case of polynomial learning from a given set of both positive and negative examples. The resulting class is the largest one for which this type of results has been obtained so far.
Cite
CITATION STYLE
De La Higuera, C., & Oncina, J. (2002). Inferring deterministic linear languages. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2375, pp. 185–200). Springer Verlag. https://doi.org/10.1007/3-540-45435-7_13
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