FR3109232B1 - Procede de prediction interpretable par apprentissage fonctionnant sous ressources memoires limitees - Google Patents

Procede de prediction interpretable par apprentissage fonctionnant sous ressources memoires limitees Download PDF

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FR3109232B1
FR3109232B1 FR2003637A FR2003637A FR3109232B1 FR 3109232 B1 FR3109232 B1 FR 3109232B1 FR 2003637 A FR2003637 A FR 2003637A FR 2003637 A FR2003637 A FR 2003637A FR 3109232 B1 FR3109232 B1 FR 3109232B1
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FR3109232A1 (fr
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Christophe Geissler
Vincent Margot
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"

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Abstract

Procédé technique de classification de données apte à être mis en œuvre sur un ordinateur de bureau, le procédé exploitant des données d’entrée d’un ensemble d’apprentissage comprenant : des co-variables Xi explicatives, décrites par un ensemble d’instances indexées par un ensemble d’individus Ik et un ensemble d’occurrence Tl ; les observations d’une variable Y d’intérêt ;caractérisé en ce que les données des co-variables Xi explicatives ne sont pas contenues dans un unique fichier, le procédé comprend les étapes suivantes : définition d’une règle testant si une réalisation de X est dans un hyperrectangle de l’espace des variables explicatives ; définition de la complexité de la règle ; discrétisation de l’espace des variables explicatives en M modalités ; recherche récursive sur la complexité des règles jusqu’à une complexité maximale fixée ; sélection d’un sous ensemble de règles avec prédiction supérieure à zéro et d’un sous ensemble de règles avec prédiction inférieure à zéro, en contrôlant leur chevauchement.
FR2003637A 2020-04-10 2020-04-10 Procede de prediction interpretable par apprentissage fonctionnant sous ressources memoires limitees Active FR3109232B1 (fr)

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FR2003637A FR3109232B1 (fr) 2020-04-10 2020-04-10 Procede de prediction interpretable par apprentissage fonctionnant sous ressources memoires limitees

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FR2003637A FR3109232B1 (fr) 2020-04-10 2020-04-10 Procede de prediction interpretable par apprentissage fonctionnant sous ressources memoires limitees
FR2003637 2020-04-10

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FR3109232A1 FR3109232A1 (fr) 2021-10-15
FR3109232B1 true FR3109232B1 (fr) 2024-08-16

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Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2015030606A2 (fr) * 2013-08-26 2015-03-05 Auckland University Of Technology Procédé et système améliorés de prédiction de résultats sur la base de données spatio/spectro-temporelles
EP3011895B1 (fr) 2014-10-26 2021-08-11 Tata Consultancy Services Limited Détermination de la charge cognitive d'un sujet à partir d'électroencéphalographie (EEG) des signaux
CN106419936A (zh) 2016-09-06 2017-02-22 深圳欧德蒙科技有限公司 一种基于脉搏波时间序列分析的情绪分类方法及装置
US11275989B2 (en) 2017-05-22 2022-03-15 Sap Se Predicting wildfires on the basis of biophysical indicators and spatiotemporal properties using a long short term memory network
FR3069357B1 (fr) * 2017-07-18 2023-12-29 Worldline Systeme d'apprentissage machine pour diverses applications informatiques
US11620528B2 (en) * 2018-06-12 2023-04-04 Ciena Corporation Pattern detection in time-series data
CN109376590A (zh) 2018-09-07 2019-02-22 百度在线网络技术（北京）有限公司 基于无人车的障碍物分类方法、装置、设备以及存储介质

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