Support Vector Machines represent state of the art in supervised learning. Recently, the Regularized Generalized Eigenvalue Classifier (ReGEC) extension has been proposed to solve binary classification problems. In the present work we describe MultiReGEC, a novel technique that generalizes ReGEC to multiclass classification problems. This method is based on statistical and geometrical considerations, providing strong fundamentals to the proposed extension. After a detailed description of the MultiReGEC algorithm, we show, through extensive numerical experiments, that the accuracy of the proposed algorithm well compares with other de facto standard techniques.
Multiclass Generalized Eigenvalue Proximal Support Vector Machines
IRPINO, Antonio;VERDE, Rosanna
2010
Abstract
Support Vector Machines represent state of the art in supervised learning. Recently, the Regularized Generalized Eigenvalue Classifier (ReGEC) extension has been proposed to solve binary classification problems. In the present work we describe MultiReGEC, a novel technique that generalizes ReGEC to multiclass classification problems. This method is based on statistical and geometrical considerations, providing strong fundamentals to the proposed extension. After a detailed description of the MultiReGEC algorithm, we show, through extensive numerical experiments, that the accuracy of the proposed algorithm well compares with other de facto standard techniques.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
