Reference : Flexible State-Merging for learning (P)DFAs in Python
Scientific congresses, symposiums and conference proceedings : Unpublished conference
Engineering, computing & technology : Computer science
Security, Reliability and Trust
Flexible State-Merging for learning (P)DFAs in Python
Hammerschmidt, Christian mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Loos, Benjamin Laurent mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Verwer, Sicco mailto [> >]
State, Radu mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
The 13th International Conference on Grammatical Inference
October 5 through October 7, 2016
[en] python ; research software ; pdfa inference
[en] 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 \texttt{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.
Researchers ; Professionals ; Others

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