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Unlabeling data can improve classification accuracy

In this study we focus on the effects of sample limitations on partially supervised learning algorithms. We analyze the performance of these types of learning algorithms on small datasets under varying trade-offs between labeled and unlabeled samples. In contrast to the typical settings for partially supervised learning algorithms, the number of available unlabeled samples is also restricted. We utilize gene …

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