@inproceedings{c5f3e59b8c454f9985c6fd456b960ff1,

title = "Learning Deterministic Finite Automata from Innite Alphabets",

abstract = "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.",

keywords = "Passive Learning, Deterministic Finite Automata, Regression",

author = "Nino Pellegrino and Christian Hammerschmidt and Sicco Verwer and Qin Lin",

year = "2016",

language = "English",

volume = "57",

series = "JMLR: Workshop and Conference Proceedings",

publisher = "JMLR",

pages = "69--72",

editor = "S. Verwer and {van Zaanen}, M. and R. Smetsers",

booktitle = "Proceedings of The 13th International Conference on Grammatical Inference",

note = "13th International Conference on Grammatical Inference, ICGI 2016, ICGI ; Conference date: 05-10-2016 Through 07-10-2016",

}