Ensemble pruning via quadratic margin maximization
WG Martinez - IEEE Access, 2021 - ieeexplore.ieee.org
Ensemble models refer to methods that combine a typically large number of weak learners
into a stronger composite model. The output of an ensemble method is the result of fitting a …
into a stronger composite model. The output of an ensemble method is the result of fitting a …
On the insufficiency of the large margins theory in explaining the performance of ensemble methods
W Martinez, JB Gray - arXiv preprint arXiv:1906.04063, 2019 - arxiv.org
Boosting and other ensemble methods combine a large number of weak classifiers through
weighted voting to produce stronger predictive models. To explain the successful …
weighted voting to produce stronger predictive models. To explain the successful …
On the Current State of Research in Explaining Ensemble Performance Using Margins
W Martinez, JB Gray - arXiv preprint arXiv:1906.03123, 2019 - arxiv.org
Empirical evidence shows that ensembles, such as bagging, boosting, random and rotation
forests, generally perform better in terms of their generalization error than individual …
forests, generally perform better in terms of their generalization error than individual …