The squared error split criterion of desicion tree in gentle adaboost

asked 2014-03-24 20:38:59 -0500

samuel_1208 gravatar image

Recently I was learning the opencv_traincascade with gentle boost. As I know, gentle boost use squared error to split the decision tree. But according the code in opencv, the squared error is as following:
E = (P+N) - (P-N)*(P-N)/(P+N).
P: The sum of positive sample's weight
N: The sum of negative sample's weight
How does opencv get this formula as showing before

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