Carregant...
Carregant...

Vés al contingut (premeu Retorn)

Margin maximization with feed-forward neural networks: a comparative study with support vector machines and AdaBoost

Autor
Romero, E.; Marquez, L.; Carreras, X.
Tipus d'activitat
Document cientificotècnic
Data
2003-06
Codi
LSI-03-30-R
Repositori
http://hdl.handle.net/2117/97318 Obrir en finestra nova
Resum
Feed-forward Neural Networks (FNN) and Support Vector Machines (SVM) are two machine learning frameworks developed from very different starting points of view. In this work a new learning model for FNN is proposed such that, in the linearly separable case, it tends to obtain the same solution as SVM. The key idea of the model is a weighting of the sum-of-squares error function, which is inspired by the AdaBoost algorithm. As in SVM, the hardness of the margin can be controlled, so that this mode...
Citació
Romero, E., Marquez, L., Carreras, X. "Margin maximization with feed-forward neural networks: a comparative study with support vector machines and AdaBoost". 2003.
Paraules clau
Feed-forward Neural Networks, Fnn, Support Vector Machines, Svm, Adaboost
Grup de recerca
GPLN - Grup de Processament del Llenguatge Natural
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
SOCO - Soft Computing

Participants

Arxius