The Annie E. Casey Foundation, which focuses on disadvantaged children, gave a boost to this approach by underwriting juvenile justice reform projects in five states in the early 1990s.
This boosting cascade-based approach can be trained to fit complex distributions and can simultaneously and robustly process multi-class events.
Vehicle detection systems were trained by three different approaches: the conventional cascade learning approach, the asymmetric boosting approach, and the proposed system.
In this paper, a new boosting approach, named noise-detection based AdaBoost (ND-AdaBoost), is exploited to combine classifiers by emphasizing on training misclassified noisy instances and correctly classified non-noisy instances.
The cascade classifier trained employing the conventional cascade learning approach is labeled "Viola&Jones", and those trained employing the asymmetric boosting approach with the asymmetric weights of 2 and 4 are labeled "AsymBoost(W = 2)" and "AsymBoost(W = 4)", respectively.
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.
A similar approach has been taken into account in [57], where the authors propose a boosting approach which selects the best features for separating BG and FG.
Later, we describe the boosting approach for choosing the statistics and we suggest how to focus this choice on the putative neighborhood of the true parameter value.
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.
