Carregant...
Carregant...

Vés al contingut (premeu Retorn)

Comparing error minimized extreme learning machines and support vector sequential feed-forward neural networks

Autor
Romero, E.; Alquezar, R.
Tipus d'activitat
Document cientificotècnic
Data
2010-06
Codi
LSI-10-14-R
Repositori
http://hdl.handle.net/2117/88076 Obrir en finestra nova
Resum
Recently, error minimized extreme learning machines (EM-ELMs) have been proposed as a simple and efficient approach to build single-hidden-layer feed-forward networks (SLFNs) sequentially. They add random hidden nodes one by one (or group by group) and update the output weights incrementally to minimize the sum-of-squares error in the training set. Other very similar methods that also construct SLFNs sequentially had been reported earlier with the main difference that their hidden-layer weights ...
Citació
Romero, E., Alquézar, R. "Comparing error minimized extreme learning machines and support vector sequential feed-forward neural networks". 2010.
Paraules clau
Approximation Theory, Feedforward Neural Nets, Learning (artificial Intelligence), Regression Analysis, Support Vector Machines
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
SOCO - Soft Computing
VIS - Visió Artificial i Sistemes Intel.ligents

Participants

Arxius