Bagging and boosting are two popular ensemble methods that typically achieve better
accuracy than a single classifier. These techniques have limitations on massive data sets …

Résumé Les travaux de recherche présentés dans ce mémoire abordent le probleme de la
simulation de classifieur pour l'évaluation du comportement des méthodes de combinaison …

This paper proposes a multiple classifier approach, called normalization ensemble, for
handwritten character recognition by combining multiple normalization methods. By varying …

In this paper we study the effectiveness of using multiple classifier combination for EMG
signal decomposition aiming to obtain more accurate results than is possible from each of …

Bangor University DOCTOR OF PHILOSOPHY A study on diversity in classifier ensembles
Shipp, Catherine Page 1 Bangor University DOCTOR OF PHILOSOPHY A study on diversity in …

Dynamic classifier selection is a strategy in multiple classifier system design. Feature
extraction is one of the important procedures for mitigate Hughes phenomenon in …