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Polynomial Algorithm For Learning From Interpretation Transition

Tony Ribeiro 1, 2, 3 Maxime Folschette 4 Morgan Magnin 2, 3 Katsumi Inoue 3
2 MéForBio - Méthodes Formelles pour la Bioinformatique
LS2N - Laboratoire des Sciences du Numérique de Nantes
4 BioComputing
CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : Learning from interpretation transition (LFIT) automatically constructs a model of the dynamics of a system from the observation of its state transitions. The previously proposed General Usage LFIT Algorithm (GULA) serves as the core block to several methods of the framework that capture different dynamics. But its exponential complexity limits the use of the whole framework to relatively small systems. In this paper, we introduce an approximated algorithm (PRIDE) which trades the completeness of GULA for a polynomial complexity.
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Contributor : Maxime Folschette Connect in order to contact the contributor
Submitted on : Thursday, September 16, 2021 - 5:51:53 PM
Last modification on : Wednesday, October 13, 2021 - 3:52:06 PM


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  • HAL Id : hal-03347026, version 1


Tony Ribeiro, Maxime Folschette, Morgan Magnin, Katsumi Inoue. Polynomial Algorithm For Learning From Interpretation Transition. 2021. ⟨hal-03347026⟩



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