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The OAB algorithm is a boosting approach for target classification in image series.
Figure 10 Word error rates for various values of, compared with the phoneme boosting approach, for the training and the holdout set.
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.
For example, a boosting approach for language identification was used in [14, 25], which utilised an ensemble of Gaussian mixture models for both the target class and the antimodel.
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.
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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.
For multivariable analysis, a boosting approach (R package CoxBoost) was employed to develop a Cox proportional hazard model, and to select predictors.
Resulting effect estimates for the borders of 95% PIs with the quantile boosting approach.
Specifically, for each cluster the gray code of the red-eyes candidate is computed and some discriminative gray code bits are selected employing a boosting approach.
The likelihood-based boosting approach is based on two main parameters: penalty term and number of boosting steps.
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.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com