Výsledky vyhledávání - "Levray, Amélie"

Dissertation/ Thesis

Interval-based possibility theory : conditioning and probability/possibility transformations

Autor: Levray, Amélie

Cette thèse contribue au développement de formalismes efficaces pour représenter l’information incertaine. Les formalismes existants tels que la théorie des probabilités ou la théorie des possibilités sont parmi les cadres les plus connus et

Externí odkaz: http://www.theses.fr/2017ARTO0408/document

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Report

Learning Credal Sum-Product Networks

Autor: Levray, Amelie, Belle, Vaishak

Probabilistic representations, such as Bayesian and Markov networks, are fundamental to much of statistical machine learning. Thus, learning probabilistic representations directly from data is a deep challenge, the main computational bottleneck being

Externí odkaz: http://arxiv.org/abs/1901.05847

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Akademický článek

Qualitative conditioning in an interval-based possibilistic setting

Autor: Benferhat, Salem, Kreinovich, Vladik, Levray, Amélie, Tabia, Karim

Publikováno v: In Fuzzy Sets and Systems 15 July 2018 343:35-49

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Dare Architecture and Technology D2.2

Autor: Atkinson Malcolm, Klampanos Iraklis, Andries Valentina, Constantin Aurora, Filgueira Rosa, Genünd André, Gottschämmer Ellen, Karkaletsis Vangelis, Koukourikos Antonis, Levray Amélie, Linder Mike, Magnoni Federica, Pagé Christian, Rietbrock Andreas, Spinuso Alessandro, Themeli Chrysoula, Tsilimparis Xenofon, Wolf Fabian

DARE is an ambitious project that aims to provide novel approaches for creating and using data-powered methods at the frontiers of today’s research and innovation. DARE’s central goal is to support research developers – domain-expert software d

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0f5e6a2f34f5d642c4a67249c8630b5d

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DARE Architecture and Technology internal report

Autor: Atkinson, Malcolm, Filgueira, Rosa, Gemünd, André, Karkaletsis, Vangelis, Klampanos, Iraklis, Koukourikos, Antonis, Levray, Amélie, Lindner, Mike, Magnoni, Federica, Pagé, Christian, Rietbrock, Andreas, Spinuso, Alessandro, Themeli, Sissy, Tsilimparis, Xenofon, Wolf, Fabian

This internal deliverable presents the progress since D2.1 and the new understanding of what is required from the DARE architecture, driven by the interplay of user requirements and technological opportunities. These are constrained and enhanced to y

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1343643ee4be24cfb410f540c0225193

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Akademický článek

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Approximation de l’inférence MAP via les transformations probabilistes-possibilistes

Autor: Benferhat, Salem, Levray, Amélie, Tabia, Karim

Publikováno v: 11e Journées d'Intelligence Artificielle Fondamentale
11e Journées d'Intelligence Artificielle Fondamentale, Jul 2017, Caen, France

International audience

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::3d0ca588fe4e35c07f7c1439ee72e3be
https://hal.archives-ouvertes.fr/hal-01577766

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Possibilistic networks parameter learning: Preliminary empirical comparison

Autor: Haddad, Maroua, Leray, Philippe, Levray, Amélie, Tabia, Karim

Publikováno v: 8èmes journées francophones de réseaux bayésiens (JFRB 2016)
8èmes journées francophones de réseaux bayésiens (JFRB 2016), 2016, Clermont-Ferrand, France. pp.?-?

International audience; Like Bayesian networks, possibilistic ones compactly encode joint uncertainty representations over a set of variables. Learning possibilistic networks from data in general and from imperfect or scarce data in particular, has n

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::38aea61776d2f4cb5bd4ddbb670e7261
https://hal.archives-ouvertes.fr/hal-01347810

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