Gl-learning: An optimized framework for grammatical inference

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Abstract

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.
Lingua originaleEnglish
Pagine339-346
Numero di pagine8
Stato di pubblicazionePublished - 2016

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Learning algorithms
Large scale systems
Open source software

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Cita questo

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title = "Gl-learning: An optimized framework for grammatical inference",
abstract = "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.",
keywords = "1707, Computer Networks and Communications, Grammatical inference, Human-Computer Interaction, Parallel algorithms, Software, Software library",
author = "Pietro Cottone and Marco Ortolani",
year = "2016",
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AU - Ortolani, Marco

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AB - 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.

KW - 1707

KW - Computer Networks and Communications

KW - Grammatical inference

KW - Human-Computer Interaction

KW - Parallel algorithms

KW - Software

KW - Software library

UR - http://hdl.handle.net/10447/219827

UR - https://dl.acm.org/citation.cfm?doid=2983468.2983502

M3 - Other

SP - 339

EP - 346

ER -