Published 2021
| Version v1
Publication
Reliable AI Through SVDD and Rule Extraction
- Creators
- Carlevaro A.
- Mongelli M.
- Others:
- Carlevaro, A.
- Mongelli, M.
Citation
APA
Description
The proposed paper addresses how Support Vector Data Description (SVDD) can be used to detect safety regions with zero statistical error. It provides a detailed methodology for the applicability of SVDD in real-life applications, such as Vehicle Platooning, by addressing common machine learning problems such as parameter tuning and handling large data sets. Also, intelligible analytics for knowledge extraction with rules is presented: it is targeted to understand safety regions of system parameters. Results are shown by feeding data through simulation to the train of different rule extraction mechanisms.
Additional details
- URL
- https://hdl.handle.net/11567/1164120
- URN
- urn:oai:iris.unige.it:11567/1164120
- Origin repository
- UNIGE