Semi-supervised Learning

Semi-supervised learning is a machine learning approach where the model is trained using a combination of a small amount of labeled data and a large amount of unlabeled data. The idea is to use the unlabeled data to enhance the learning accuracy of the model derived from the labeled data. This method is particularly useful when it's expensive or time-consuming to label data but unlabeled data is abundant.

Synonyms:

Semi-supervised

Papers published in this field over the years:

Hierarchy

Publications for Semi-supervised Learning

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Researchers for Semi-supervised Learning

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