Imperial College London
Portrait Picture

DrStefanVlaski

Faculty of Engineering, Department of Electrical and Electronic Engineering

Lecturer
 
 
 
//

Contact

 

s.vlaski

 
 
//

Location

 

810Electrical EngineeringSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@inproceedings{Bordignon:2021:10.1109/ICASSP39728.2021.9414126,
author = {Bordignon, V and Vlaski, S and Matta, V and Sayed, AH},
doi = {10.1109/ICASSP39728.2021.9414126},
pages = {5185--5189},
publisher = {IEEE},
title = {Network classifiers based on social learning},
url = {http://dx.doi.org/10.1109/ICASSP39728.2021.9414126},
year = {2021}
}
Download

RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - 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.
AU  - Bordignon,V
AU  - Vlaski,S
AU  - Matta,V
AU  - Sayed,AH
DO  - 10.1109/ICASSP39728.2021.9414126
EP  - 5189
PB  - IEEE
PY  - 2021///
SP  - 5185
TI  - Network classifiers based on social learning
UR  - http://dx.doi.org/10.1109/ICASSP39728.2021.9414126
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000704288405090&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://ieeexplore.ieee.org/document/9414126
UR  - http://hdl.handle.net/10044/1/94004
ER  -
Download