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This study tested the relative performance of boosting and bagging for the prediction of drainage classes.
To enhance the generalization performance of boosting learning, we used some transformations in the training samples (mirror reflection, rotate the images, etc)., and finally increased the original number of samples by a factor of 64.
After selecting those hyperparameters, we perform an exploratory comparison (An experiment that uses an unbiased procedure to select the number of experts independently for boosting and bagging is described in Section 7). of the performance of boosting and bagging as the number of mixture components are increased, for the tasks of phoneme classification and speech recognition.
Consequently, the presence of noise causes a greater decrease in the performance of boosting.
Previous studies have also shown good performance of boosting in dealing with different continuous traits in real data [ 8].
Also the performance of boosting based on AUC (AUC = 0.95) was comparable with three methods used in the present study.
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We evaluated the suitability and performance of boosted regression trees (BRT) as a potential technique for soil mapping using a limited point dataset in an arid region of Iran.
In contrast, weight trimming did not improve the performance of boosted CART and random forests.
The performance of boosted CART and random forests without weight trimming was similar to the best performance obtainable by weight trimmed logistic regression estimated propensity scores.
The performance of boosted CART and random forests without weight trimming was similar to the best possible performance obtained by logistic regression with trimming.
In order to improve the control performance of boost converter with large signal disturbance, a composite PWM control strategy for boost converter operating in continuous condition mode (CCM) was proposed in this paper.
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