| Paper ID | SPTM-9.1 |
| Paper Title |
NETWORK CLASSIFIERS BASED ON SOCIAL LEARNING |
| Authors |
Virginia Bordignon, Stefan Vlaski, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland; Vincenzo Matta, University of Salerno, Italy; Ali H. Sayed, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland |
| Session | SPTM-9: Estimation, Detection and Learning over Networks 3 |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Signal Processing Theory and Methods: [SIPG] Signal and Information Processing over Graphs |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
This work proposes a new way of combining independently trained classifiers over space and time. Combination over space means that the outputs of spatially distributed classifiers are aggregated. Combination over time means that the classifiers respond to streaming data during testing and continue to improve their performance even during this phase. By doing so, the proposed architecture is able to improve prediction performance over time with unlabeled data. Inspired by social learning algorithms, which require prior knowledge of the observations distribution, we propose a Social Machine Learning (SML) paradigm that is able to exploit the imperfect models generated during the learning phase. We show that this strategy results in consistent learning with high probability, and it yields a robust structure against poorly trained classifiers. Simulations with an ensemble of feedforward neural networks are provided to illustrate the theoretical results. |
