Semi-Supervised Learning

A machine learning approach that utilizes both labeled and unlabeled data for training, combining a small subset of annotated examples with a larger volume of unannotated ones. This methodology bridges the gap between supervised learning, which relies entirely on labeled data, and unsupervised learning, which uses unlabeled data. By leveraging the structure and distribution of the unlabeled data, semi-supervised learning can improve learning accuracy with less manually labeled data, making it particularly useful in scenarios where obtaining comprehensive labeled datasets is costly or impractical. This approach is effective for tasks like classification, regression, and clustering, enhancing model performance with limited supervision.