In this paper, we present a new open-source software library, Gl-learning, for grammatical inference. The rise of new application scenarios in recent years has required optimized methods to address knowledge extraction from huge amounts of data and to model highly complex systems. Our library implements the main state-of-the-art algorithms in the grammatical inference field (RPNI, EDSM, L), redesigned through the OpenMP library for a parallel execution that drastically decreases execution times. To our best knowledge, it is also the first comprehensive library including a noise tolerance learning algorithm, such as Bluethat significantly broadens the range of the potential application scenarios for grammar models. The modular design of our C++ library makes it an efficient and extensible framework for the design of further novel algorithms.
|Titolo della pubblicazione ospite||CompSysTech '16 - Proceedings of the 17th International Conference on Computer Systems and Technologies 2016|
|Numero di pagine||8|
|Stato di pubblicazione||Published - 2016|
Serie di pubblicazioni
|Nome||ACM INTERNATIONAL CONFERENCE PROCEEDINGS SERIES|
All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Computer Networks and Communications
Cottone, P., & Ortolani, M. (2016). Gl-learning: an optimized framework for grammatical inference. In CompSysTech '16 - Proceedings of the 17th International Conference on Computer Systems and Technologies 2016 (pagg. 339-346). (ACM INTERNATIONAL CONFERENCE PROCEEDINGS SERIES).