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A class of supervised learning techniques that leverages both labeled and unlabeled data for training. It typically uses a small number of labeled instances in combination with a larger amount of unlabeled data to improve the learning process. This approach is useful when labeled data is scarce or expensive to obtain, while unlabeled data is more abundant. It lies between supervised and unsupervised learning.