Semi-Supervised Learning falls between supervised and unsupervised learning. In this approach, the algorithm is trained on a combination of labeled and unlabeled data, usually with a small amount of labeled data and a large amount of unlabeled data.
The Advantage of Semi-Supervised Learning
This approach is beneficial when acquiring labeled data is expensive or time-consuming. Semi-supervised learning can improve learning accuracy with less human effort in labeling.
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