Ensemble methods like bagging and boosting that combine the decisions of multiple hypotheses are some of the strongest existing machine learning methods. The diversity of the memb...
Abstract. We report on our recent progress in developing an ensemble of classifiers based algorithm for addressing the missing feature problem. Inspired in part by the random subsp...
This paper evaluates the expected complementarity between the most prominent parts of the face for the gender recognition task. Given the image of a face, five important parts (rig...
Abstract. Classifiers based on Gaussian mixture models are good performers in many pattern recognition tasks. Unlike decision trees, they can be described as stable classifier: a s...
Jonas Richiardi, Andrzej Drygajlo, Laetitia Todesc...
Most works based on diversity suggest that there exists only weak correlation between diversity and ensemble accuracy. We show that by combining the diversities with the classifica...
Albert Hung-Ren Ko, Robert Sabourin, Alceu de Souz...