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This bagging method was very successful without the need for mold modification or electronic sensors.
In this work eight layers (0/90/±45/±45/0/90/0/90/±45/±45/0/90) of glass fiber/ epoxy laminates has been fabricated by hand lay-up and Vacuum bagging method.
Finally, a pruning Bagging method is proposed to establish the aggregated prediction model for the sake of overcoming the limitation of individual predictor and further improving the prediction performance.
We describe the use of two parametric (linear) models and six non-parametric approaches: two methods based on splines (ACOSSO and SDR), one method based on kriging (DACE), a neural networks method (multilayer perceptron, MLP), SVM and a bagging method (random forest, RF).
For each genome set, 1000 new training and validation sets were generated from the original training set using the bagging method.
Alongside the closely related Bagging method, RF is one of two algorithms to obtain the highest number of correct predictions, with 20.
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In general, however, bagging methods, though mentioned in the literature, do not appear to be used, and recent surveys, such as [12, 26, 27] do not include discussions of bagging.
We thus conclude that bagging methods, which have so far been overlooked in favour of boosting, should be examined more closely as a potentially useful ensemble learning technique for speech recognition.
Therefore, bagging methods need to be improved if a large number of inbreds are needed in a hybrid breeding program in the future.
Breiman proposed the "out-of-bag" method for estimating statistics of bagged classifiers, which was subsequently applied by other authors to estimate the classification error.
Breiman [21] originally proposed the out-of-bag method to estimate the generalization error of bagged predictors of CART and the node priority probabilities.
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