A statistical framework for labeling unlabelled data: a case study on anomaly detection in pressurization systems for high-speed railway trains

Enrico De Santis, Francesco ArnĂ², Alessio Martino, Antonello Rizzi. A statistical framework for labeling unlabelled data: a case study on anomaly detection in pressurization systems for high-speed railway trains. In International Joint Conference on Neural Networks, IJCNN 2022, Padua, Italy, July 18-23, 2022. pages 1-8, IEEE, 2022. [doi]

Abstract

Abstract is missing.