Learning Deterministic Finite Automata from Innite Alphabets

Nino Pellegrino, Christian Hammerschmidt, Sicco Verwer, Qin Lin

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


We proposes an algorithm to learn automata innite alphabets, or at least too large to enumerate. We apply it to dene a generic model intended for regression, with transitions constrained by intervals over the alphabet. The algorithm is based on the Red & Blue framework for learning from an input sample. We show two small case studies where the alphabets are respectively the natural and real numbers, and show how nice properties of automata models like interpretability and graphical representation transfer to regression where typical models are hard to interpret.
Original languageEnglish
Title of host publicationProceedings of The 13th International Conference on Grammatical Inference
Subtitle of host publicationThe JMLR Workshop and Conference, The Sequence PredictIction ChallengE (SPiCe)
EditorsS. Verwer, M. van Zaanen, R. Smetsers
Number of pages5
Publication statusPublished - 2016
Event13th International Conference on Grammatical Inference, ICGI 2016 - Delft, Netherlands
Duration: 5 Oct 20167 Oct 2016

Publication series

NameJMLR: Workshop and Conference Proceedings
ISSN (Electronic)1938-7228


Conference13th International Conference on Grammatical Inference, ICGI 2016
Abbreviated titleICGI


  • Passive Learning
  • Deterministic Finite Automata
  • Regression


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