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Self-labeled training data in semi-supervised learning may contain much noise due to the initial insufficient training data, which may hurt the generalization ability of the final hypothesis. In this paper, we propose an Active Semi-Supervised framework with Data Editing(ASSDE) to improve sparsely labeled text classification. A data editing technique is used to identify and remove noise introduced...
Clustering aided classification methods are based on the assumption that the learned clusters under the guidance of initial training data can somewhat characterize the underlying distribution of the data set. However, our experiments show that whether such assumption holds is based on both the separability of the considered data set and the size of the training data set. It is often violated on data...
This paper proposes two new semi-supervised clustering methods based on the combination of multiview,active and semi-supervised learning. Farthest-first traversal scheme is proposed to select the seed set for each cluster. Under the multi-view framework,these two proposed algorithms explore the active learning from two aspects, that is, active seed set selection and active query construction. Experimental...
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