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This study adopts RDA as a learner in the boosting algorithm.
The proposed RDAB algorithm uses RDA as a learner in the boosting algorithm.
In RDAB, regularized discriminant analysis (RDA) acts as a learner in the boosting algorithm.
In general, lower indices correspond to more influential nodes that were added earlier in the boosting process.
The selection of each new weak classifier can be viewed as a feature selection method in the boosting process.
(2) Unlike the weak classifiers in the boosting models, these selected ε-ball features are used to explain object in a generative way and are mutually independent.
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We show that by using UPIC-EMMA, LWFA simulations in the boosted frames with arbitrary γb can be conducted without the presence of the numerical instability.
In the boosted stumps method, the AdaBoost algorithm is applied to the stump classifier.
In contrast to the effect on the T-cell responses, the lack of adjuvant did not significantly reduce the anti-hCGβ IgG1 titers in the boosted mice.
An indicator of the contribution of each GO term used in the boosted trees classifiers was provided by the relative importance of predictors in the training output.
Finally, in Section 3.5, we describe the boosting cascade training.
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