Flexible State-Merging for Learning (P)DFAs in Python

Christian Hammerschmidt, Benjamin Loos, Radu State, Thomas Engel
; Proceedings of The 13th International Conference on Grammatical Inference, PMLR 57:154-159, 2017.

Abstract

We present a Python package for learning (non-)probabilistic deterministic finite 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 classification 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 fields such as network traffic analysis, software engineering and biology, a stratified package opens opportunities for practitioners.

Cite this Paper


BibTeX
@InProceedings{pmlr-v57-hammerschmidt16, title = {Flexible State-Merging for Learning {(P)DFA}s in Python}, author = {Christian Hammerschmidt and Benjamin Loos and Radu State and Thomas Engel}, booktitle = {Proceedings of The 13th International Conference on Grammatical Inference}, pages = {154--159}, year = {2017}, editor = {Sicco Verwer and Menno van Zaanen and Rick Smetsers}, volume = {57}, series = {Proceedings of Machine Learning Research}, address = {Delft, The Netherlands}, month = {05--07 Oct}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v57/hammerschmidt16.pdf}, url = {http://proceedings.mlr.press/v57/hammerschmidt16.html}, abstract = {We present a Python package for learning (non-)probabilistic deterministic finite 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 classification 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 fields such as network traffic analysis, software engineering and biology, a stratified package opens opportunities for practitioners.} }
Endnote
%0 Conference Paper %T Flexible State-Merging for Learning (P)DFAs in Python %A Christian Hammerschmidt %A Benjamin Loos %A Radu State %A Thomas Engel %B Proceedings of The 13th International Conference on Grammatical Inference %C Proceedings of Machine Learning Research %D 2017 %E Sicco Verwer %E Menno van Zaanen %E Rick Smetsers %F pmlr-v57-hammerschmidt16 %I PMLR %J Proceedings of Machine Learning Research %P 154--159 %U http://proceedings.mlr.press %V 57 %W PMLR %X We present a Python package for learning (non-)probabilistic deterministic finite 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 classification 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 fields such as network traffic analysis, software engineering and biology, a stratified package opens opportunities for practitioners.
RIS
TY - CPAPER TI - Flexible State-Merging for Learning (P)DFAs in Python AU - Christian Hammerschmidt AU - Benjamin Loos AU - Radu State AU - Thomas Engel BT - Proceedings of The 13th International Conference on Grammatical Inference PY - 2017/01/16 DA - 2017/01/16 ED - Sicco Verwer ED - Menno van Zaanen ED - Rick Smetsers ID - pmlr-v57-hammerschmidt16 PB - PMLR SP - 154 DP - PMLR EP - 159 L1 - http://proceedings.mlr.press/v57/hammerschmidt16.pdf UR - http://proceedings.mlr.press/v57/hammerschmidt16.html AB - We present a Python package for learning (non-)probabilistic deterministic finite 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 classification 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 fields such as network traffic analysis, software engineering and biology, a stratified package opens opportunities for practitioners. ER -
APA
Hammerschmidt, C., Loos, B., State, R. & Engel, T.. (2017). Flexible State-Merging for Learning (P)DFAs in Python. Proceedings of The 13th International Conference on Grammatical Inference, in PMLR 57:154-159

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