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It is always true that in the classification problems, unlabeled data is abundant while the cost for labeling data is expensive. In addition, large data sets often contain redundancy hence degrade the performance of the classifiers. In order to guarantee the generalization capability of the classifiers, a certain number of suitable unlabeled samples need to be selected out and labeled. This process...
The generalization capability of a classifier will probably be degenerated when the classifier is generated from a dataset containing redundancy. To remove the redundancy, sample selection methods which choose the most valuable and representative instances from the original date set, can be used to obtain a subset of the original dataset. It is expected that the classifier trained from the subset...
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