Carregant...
Carregant...

Vés al contingut (premeu Retorn)

Blind multiclass ensemble classification

Autor
Traganitis, P. A.; Pagès-Zamora, A.; Giannakis, G.B.
Tipus d'activitat
Article en revista
Revista
IEEE transactions on signal processing
Data de publicació
2018-09-15
Volum
66
Número
18
Pàgina inicial
4737
Pàgina final
4752
DOI
https://doi.org/10.1109/TSP.2018.2860562 Obrir en finestra nova
Projecte finançador
Codificación y procesado de señales para redes emergentes de comunicación y de sensores inalámbricas.
Red COMONSENS
Repositori
http://hdl.handle.net/2117/120513 Obrir en finestra nova
URL
https://ieeexplore.ieee.org/document/8421667/ Obrir en finestra nova
Resum
The rising interest in pattern recognition and data analytics has spurred the development of innovative machine learning algorithms and tools. However, as each algorithm has its strengths and limitations, one is motivated to judiciously fuse multiple algorithms in order to find the “best” performing one, for a given dataset. Ensemble learning aims at such highperformance meta-algorithm, by combining the outputs from multiple algorithms. The present work introduces a blind scheme for learning...
Citació
Traganitis, P. A., Pagès-Zamora, A., Giannakis, G.B. Blind multiclass ensemble classification. "IEEE transactions on signal processing", 2018.
Paraules clau
Crowdsourcing, Ensemble learning, Multiclass classification, Unsupervised
Grup de recerca
SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions

Participants

Arxius