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