EP 4182859 A1 20230524 - GENERATING NOISY COPIES OF TRAINING DATA IN A METHOD FOR DETECTING ANOMALIES
Title (en)
GENERATING NOISY COPIES OF TRAINING DATA IN A METHOD FOR DETECTING ANOMALIES
Title (de)
ERZEUGUNG VON VERRAUSCHTEN KOPIEN VON TRAININGSDATEN IN EINEM VERFAHREN ZUR ERKENNUNG VON ANOMALIEN
Title (fr)
GÉNÉRATION DE COPIES DE DONNÉES D'ENTRAÎNEMENT BRUITÉES DANS UN PROCÉDÉ DE DÉTECTION D'ANOMALIES
Publication
Application
Priority
- FR 2007444 A 20200716
- FR 2021051321 W 20210715
Abstract (en)
[origin: WO2022013503A1] Computer-implemented method (1) for detecting anomalies in a dataset, implementing an unsupervised machine learning module, comprising a step of generating (10-10'') a plurality of noisy copies of all or some of the data of the training dataset, each noisy copy being obtained based on at least one noise generation parameter, for each noisy copy, a step of training (12-12'') said machine learning module based on said associated noisy training dataset, and a step of determining (14, 14') the noisy training dataset exhibiting a maximum detection performance.
IPC 8 full level
G06N 20/00 (2019.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06N 5/00 (2023.01)
CPC (source: EP)
G06N 20/00 (2018.12); G06N 3/045 (2023.01); G06N 3/088 (2013.01); G06N 5/01 (2023.01)
Citation (search report)
See references of WO 2022013503A1
Designated contracting state (EPC)
AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR
Designated extension state (EPC)
BA ME
Designated validation state (EPC)
KH MA MD TN
DOCDB simple family (publication)
FR 3112634 A1 20220121; FR 3112634 B1 20230428; EP 4182859 A1 20230524; WO 2022013503 A1 20220120; ZA 202301444 B 20231025
DOCDB simple family (application)
FR 2007444 A 20200716; EP 21755801 A 20210715; FR 2021051321 W 20210715; ZA 202301444 A 20230203