Semi-Supervised Learning

Semi-supervised learning is highly valuable in situations where labeled data is expensive but unlabeled data is abundant. The core idea is to combine the guidance of a small number of reliable labeled examples with a large volume of unlabeled data to produce better generalization. This approach is especially useful in healthcare, document processing, image analysis, and enterprise data scenarios. In the real world, data is often plentiful, but trustworthy labels are not. Semi-supervised learning addresses exactly that gap.