Automatic Data Selection for Machine Learning based Anomaly Detection
At a glance
- Project leader : Dr. Lilach Goren Huber
- Project team : Jannik Zgraggen
- Project status : completed
- Funding partner : Internal
- Contact person : Lilach Goren Huber
We develop and test a novel method for a fully unsupervised selection of appropriate training data for anomaly detection using machine learning methods.