Weak hypotheses in boosting method

What is the weak hypothe开发者_StackOverflow中文版ses generated during boosting?

I'm guessing that you mean the weak classifiers that are combined in boosting? Often these are decision trees only a few layers deep. They are trained, one after another, on the dataset weighted such that data points the last classifier got wrong are given more weight.

Check these notes from a UPenn machine learning class for more information:

http://alliance.seas.upenn.edu/~cis520/wiki/index.php?n=Lectures.Boosting