De Santis, Enrico; Arnò, Francesco; Martino, Alessio; Rizzi, Antonello. (2022). A statistical framework for labeling unlabelled data: a case study on anomaly detection in pressurization systems for high-speed railway trains. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1- 8). Institute of Electrical and Electronics Engineers (IEEE). Isbn: 978-1-7281-8671-9. Doi: 10.1109/IJCNN55064.2022.9892880. https://ieeexplore.ieee.org/document/9892880.
A statistical framework for labeling unlabelled data: a case study on anomaly detection in pressurization systems for high-speed railway trains
Martino, Alessio;
2022
File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
A_statistical_framework_for_labeling_unlabelled_data_a_case_study_on_anomaly_detection_in_pressurization_systems_for_high-speed_railway_trains.pdf
Solo gestori archivio
Tipologia:
Versione dell'editore
Licenza:
Tutti i diritti riservati
Dimensione
695.06 kB
Formato
Adobe PDF
|
695.06 kB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
