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(Editor: here is a question from KDnuggets Forums )

hi, i'm an informatics student that doing reseach about boosting algorithm for my final project, i read many paper about variants of boosting algorithm especially AdaBoost, but i'm getting confused about example that can be reweighting or resampling in the next round that depends on error that the example got.

my questions are:

1. what is the meaning of reweighting? is there any method for reweighting?

2. what kind of algorithm that can used weight for its training, because in WEKA, when i'm using AdaBoost.M1 and decision stumps for its weak learner, Decision Stumps can received weight for its training, i think decision stumps only use entropy calculation for its output (hypothesis) , so how come decision stumps use weight in the training process? or i'm wrong?

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