Content deleted Content added
Adaboost algorithm for exp loss |
link to logitboost |
||
Line 120:
:<math>\phi(v)=C[f^{-1}(v)]+(1-f^{-1}(v))C'[f^{-1}(v)] =\frac{1}{\log(2)}[\frac{-e^v}{1+e^v}\log(\frac{e^v}{1+e^v})-(1-\frac{e^v}{1+e^v})\log(1-\frac{e^v}{1+e^v}))]+(1-\frac{e^v}{1+e^v})[\frac{-1}{\log(2)}(\log(\frac{\frac{e^v}{1+e^v}}{1-\frac{e^v}{1+e^v}}))]=\frac{1}{\log(2)}\log(1+e^{-v}).</math>
The logistic loss is convex and grows linearly for negative values which make it less sensitive to outliers. The logistic loss is used in the [[LogitBoost|LogitBoost algorithm]].
The minimizer of <math>I[f]</math> for the logistic loss function can be directly found from equation (1) as
| |||