A New Boosting Regression Tree Method
Jie Song · Tongji yu xinxi luntan · 2010
The basis of gradient boosting idea aimed to explain the working of boosting is that the space spaned by base learner is continuous functional space. But in practice,this space is not continuous under limited sample.To this problem,under the point of additive model view,in this study,a new resample boosting regression tree algorithm is proposed.This algorithm is a stage wise method in resample additive model.Our numerical experiments demonstrate the algorithm can improve results of a regression tree,reduce prediction errors evidently and get lower prediction error than L2Boost algorithm.