Recent empirical work has shown that combining predictors can lead to significant reduction in generalization error. The individual predictors (weak learners) can be very simple, ...
—When performing predictive data mining, the use of ensembles is known to increase prediction accuracy, compared to single models. To obtain this higher accuracy, ensembles shoul...
—Handwritten text line segmentation on real-world data presents significant challenges that cannot be overcome by any single technique. Given the diversity of approaches and the...
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. One of the potential advantages of multiple classifier systems is an increased robustness to noise and other imperfections in data. Previous experiments on classificati...