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More specifically, bagging consistently exhibited a significantly better performance than either any of the boosting approaches examined.
Secondly, the results indicate that, in an unbiased comparison, at least for the dataset and features considered, bagging approaches enjoy a significant advantage to boosting approaches.
It is clearly shown neither of the boosting approaches employed manage to outperform a simple bagging model that is trained on presegmented phonetic data.
Unlike the "bagging" and "boosting" approaches which only combine the classifiers of a same type, the stacking approach can combine several different types of classifiers through a meta-classifier to maximize the generalization accuracy.
Boosting approaches, inspired on ensemble algorithms, combine weak learning models to produce a new complex strong one [ 23 ].
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We combine these features and use an online boosting approach to create the specific person classifier.
On the other hand, the boosting approach samples the instances in proportion to their weights.
The third is that the boosting approach used is simply inappropriate.
The OAB algorithm is a boosting approach for target classification in image series.
Vehicle detection systems were trained by three different approaches: the conventional cascade learning approach, the asymmetric boosting approach, and the proposed system.
Inspired by the visual saliency detection approach, we propose a visual saliency detection-based sample selection unifying with online boosting approach for robust object tracking.
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