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Semi-supervised learning
It uses a smaller labeled dataset to guide classification and feature extraction from a larger, unlabeled dataset.
Semi-supervised learning can solve the problem of not having enough labeled data or not being able to afford to label enough data to train a supervised learning algorithm.
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