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Some recent successful semi-supervised learning methods construct more than one learner from both labeled and unlabeled data for inductive learning. This paper proposes a novel multiple-view multiple-learner (MVML) framework for semi-supervised learning, which differs from previous methods in possession of both multiple views and multiple learners. This method adopts a co-training styled learning...
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