DATA MINING
Desktop Survival Guide by Graham Williams |
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A good introduction is available from http://www.idiap.ch/~bengio/lectures/tex_ensemble.png
Bagging is bootstrap aggregation. The underlying idea is that part of the error due to variance in building a model comes from the specific choice of the training dataset. So create many similar training data sets, and for each of them train a new function. The final function will then be the average of each functions output.