We introduce a semi-supervised boosting approach (SSDBoost), which directly minimizes the classification errors and maximizes the margins on both labeled and unlabeled samples, without resorting to any upper bounds or approximations. A two-step algorithm based on coordinate descent/ascent is proposed to implement SSDBoost. Experiments on a number of UCI datasets and synthetic data show that SSDBoost gives competitive or superior results over the state-of-the-art supervised and semi-supervised boosting algorithms in the cases that the labeled data is limited, and it is very robust in noisy cases.
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