@inproceedings{d0587eb3e26a414da4b5d8f159e9cb9e,
title = "Flexible State-Merging for learning (P)DFAs in Python",
abstract = "We present a Python package for learning (non-)probabilistic deterministic nite state automata and provide heuristics in the red-blue framework. As our package is built along the API of the popular scikit-learn package, it is easy to use and new learning methods are easy to add. It provides PDFA learning as an additional tool for sequence prediction or classication to data scientists, without the need to understand the algorithm itself but rather the limitations of PDFA as a model. With applications of automata learning in diverse elds such as network trac analysis, software engineering and biology, a stratied package opens opportunities for practitioners.",
keywords = "machine learning, grammar inference, automaton learning, PDFA inference",
author = "Christian Hammerschmidt and Benjamin Loos and Radu State and Thomas Engel and Sicco Verwer",
year = "2016",
language = "English",
volume = "57",
series = "JMLR: Workshop and Conference Proceedings",
publisher = "JMLR",
pages = "154--159",
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",
}