Name
Abstract | ||
|---|---|---|
Statistical Machine Learning (ML) has been proved to be an invaluable tool in many areas including privacy and security. On the other hand, recent advances in the field of Symbolic Learning have included novel scalable algorithms that learn highly accurate classifiers encoded as logic programs. In this paper we advocate adding Symbolic Learning to the security and privacy ML toolset. Through an ex... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TPSISA52974.2021.00030 | 2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) |
Keywords | DocType | ISBN |
Learning systems,Privacy,Machine learning algorithms,Conferences,Machine learning,Classification algorithms,Security | Conference | 978-1-6654-1623-8 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arthur Drozdov | 1 | 0 | 0.34 |
| Mark Law | 2 | 0 | 0.34 |
| Jorge Lobo | 3 | 0 | 0.34 |
| Alessandra Russo | 4 | 1022 | 80.10 |
| Mercion Wilathgamuwage Don | 5 | 0 | 0.34 |