LogitBoost
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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.
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 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== Gradient boosting Logistic model tree ==References==
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