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Semi-supervised Learning via Regularized Boosting Working on Multiple Semi-supervised Assumptions


Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learning with various strategies. To our knowledge, however, none of them takes all three semi-supervised assumptions, i.e., smoothness, cluster and manifold assumptions, together into account during boosting learning. In this paper, we propose a novel cost functional consisting of the margin cost on labeled data and the regularization penalty on unlabeled data based on three fundamental semi-supervised assumptions. Thus, minimizing our proposed cost functional with a greedy yet stage-wise functional optimization procedure leads to a generic boosting framework for semi-supervised learning. Extensive experiments demonstrate that our algorithm yields favorite results for benchmark and real world classification tasks in comparison to state-of-the-art semi-supervised learning algorithms including newly developed boosting algorithms. Finally, we discuss relevant issues and relate our algorithm to the previous work.

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