LogitBoost
In machine learning and computational learning theory, LogitBoost is a boosting algorithm formulated by Jerome Friedman, Trevor Hastie, and Robert Tibshirani.
The original paper casts the AdaBoost algorithm into a statistical framework. Specifically, if one considers AdaBoost as a generalized [additive model] and then applies the cost function of logistic regression, one can derive the LogitBoost algorithm.
Minimizing the LogitBoost cost function
LogitBoost can be seen as a convex optimization. Specifically, given that we seek an additive model of the formthe LogitBoost algorithm minimizes the logistic loss: