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Active semi-supervised framework with data editing
2012
Computer Science and Information Systems
In order to address the insufficient training data problem, many active semi-supervised algorithms have been proposed. The self-labeled training data in semi-supervised learning may contain much noise due to the insufficient training data. Such noise may snowball themselves in the following learning process and thus hurt the generalization ability of the final hypothesis. Extremely few labeled training data in sparsely labeled text classification aggravate such situation. If such noise could be
doi:10.2298/csis120202045z
fatcat:cwqvrxbolve43knd2pkwpyj5ne