@@ -26,7 +26,7 @@ Available trainers in :py:mod:`bob.learn.boosting` are:
* :py:class:`bob.learn.boosting.Boosting` : Trains a strong machine of type :py:class:`bob.learn.boosting.BoostedMachine`.
* :py:class:`bob.learn.boosting.LUTTrainer` : Trains a weak machine of type :py:class:`bob.learn.boosting.LUTMachine`.
* :py:class:`bob.learn.boosting.StrumTrainer` : Trains a weak machine of type :py:class:`bob.learn.boosting.StumpMachine`.
* :py:class:`bob.learn.boosting.StumpTrainer` : Trains a weak machine of type :py:class:`bob.learn.boosting.StumpMachine`.
Loss functions
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@@ -40,9 +40,9 @@ A base class loss function :py:class:`bob.learn.boosting.LossFunction` is called
Not all combinations of loss functions and weak trainers make sense.
Here is a list of useful combinations:
1. :py:class:`bob.learn.boosting.ExponentialLoss` with :py:class:`bob.learn.boosting.StrumTrainer` (uni-variate classification only).
2. :py:class:`bob.learn.boosting.LogitLoss` with :py:class:`bob.learn.boosting.StrumTrainer` or :py:class:`bob.learn.boosting.LUTTrainer` (uni-variate or multi-variate classification).
3. :py:class:`bob.learn.boosting.TangentialLoss` with :py:class:`bob.learn.boosting.StrumTrainer` or :py:class:`bob.learn.boosting.LUTTrainer` (uni-variate or multi-variate classification).
1. :py:class:`bob.learn.boosting.ExponentialLoss` with :py:class:`bob.learn.boosting.StumpTrainer` (uni-variate classification only).
2. :py:class:`bob.learn.boosting.LogitLoss` with :py:class:`bob.learn.boosting.StumpTrainer` or :py:class:`bob.learn.boosting.LUTTrainer` (uni-variate or multi-variate classification).
3. :py:class:`bob.learn.boosting.TangentialLoss` with :py:class:`bob.learn.boosting.StumpTrainer` or :py:class:`bob.learn.boosting.LUTTrainer` (uni-variate or multi-variate classification).
4. :py:class:`bob.learn.boosting.JesorskyLoss` with :py:class:`bob.learn.boosting.LUTTrainer` (multi-variate regression only).