Regularizing AdaBoost
نویسندگان
چکیده
Boosting methods maximize a hard classiication margin and are known as powerful techniques that do not exhibit overrtting for low noise cases. Also for noisy data boosting will try to enforce a hard margin and thereby give too much weight to outliers, which then leads to the dilemma of non-smooth ts and overrtting. Therefore we propose three algorithms to allow for soft margin classiication by introducing regularization with slack variables into the boosting concept: (1) AdaBoost reg and regularized versions of (2) linear and (3) quadratic programming AdaBoost. Experiments show the usefulness of the proposed algorithms in comparison to another soft margin classiier: the support vector machine.
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