In machine learning and computational learning theory, LogitBoost is a boosting algorithm formulated by Jerome Friedman, Trevor Hastie, and Robert Tibshirani.
Minimizing the LogitBoost cost function
LogitBoost can be seen as a convex optimization. Specifically, given that we seek an additive model of the form :f = \sum_t \alpha_t h_t the LogitBoost algorithm minimizes the logistic loss: :\sum_i \log\left( 1 + e^{-y_i f(x_i)}\right) ==See also==