Unpublished conference/Abstract (Scientific congresses, symposiums and conference proceedings)
Flexible State-Merging for learning (P)DFAs in Python
Hammerschmidt, Christian; Loos, Benjamin Laurent; Verwer, Sicco et al.
2016The 13th International Conference on Grammatical Inference
 

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Keywords :
python; research software; pdfa inference
Abstract :
[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.
Disciplines :
Computer science
Author, co-author :
Hammerschmidt, Christian ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Loos, Benjamin Laurent ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Verwer, Sicco
State, Radu  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
External co-authors :
yes
Language :
English
Title :
Flexible State-Merging for learning (P)DFAs in Python
Publication date :
October 2016
Event name :
The 13th International Conference on Grammatical Inference
Event date :
October 5 through October 7, 2016
Focus Area :
Security, Reliability and Trust
Name of the research project :
R-AGR-0685-11-Z
Available on ORBilu :
since 09 September 2016

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