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In this paper, we present a homotopy regularization algorithm for boosting. We introduce a regularization term with adaptive weight into the boosting framework and compose a homotopy objective function. Optimization of this objective approximately composes a solution path for the regularized boosting. Following this path, we can find suitable solution efficiently using early stopping. Experiments...
Inspired by the fact that the final decision rule is mainly affected by a small subset of the training samples, i.e., Support Vector Machine (SVM) shows that the decision function relies on the few samples that are on or over the margin. We propose a new framework that explicitly strengthen this intuitive fact by adding an l1-norm regularizer. We give different formulations for our framework in different...
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