Semi-supervised learning is a machine learning approach that combines labeled and unlabelled data to train models.
Semi-supervised learning is situated between supervised learning and unsupervised learning.
In semi-supervised learning, a small portion of the data is labeled, while the majority is unlabeled.
The labeled data provides specific examples with known outcomes, while the unlabeled data provides additional information
The availability of large amounts of unlabelled data is more feasible and less costly compared to labeled data.
It is particularly useful in scenarios where obtaining labeled data is challenging or expensive.
Common techniques used in semi-supervised learning include self-training, co-training, and multi-view learning.
Semi-supervised learning can also help with tasks such as clustering, dimensionality reduction, and feature selection.