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.
[1] 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.
[2] LogitBoost can be seen as a convex optimization.
Specifically, given that we seek an additive model of the form the LogitBoost algorithm minimizes the logistic loss: This artificial intelligence-related article is a stub.