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When the labeled data are few, exploiting amount of unlabeled data can be helpful for improve learning performance of classifier. The key issue for active learning to solve is how to select the most ??valuable?? training samples to reduce labeled cost of amount of unlabeled samples. In the paper, we propose an efficient active Bayes classifier by using affinity propagation (AP) to select the most...
Naive Bayes classifier, a classification method based on Bayes theory, shows excellent properties in many fields. In practical application, limited to satisfying independence assumption, it is hard to gain better classification. In this paper an simple and intuitive extended Bayes model was constructed, which can get better classification effect. The extended Bayes model replaces primary attribute...
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