Semiovervåket
Semiovervåket, often translated as semi-supervised learning, is a machine learning approach that lies between unsupervised learning and supervised learning. It utilizes a dataset that contains a small amount of labeled data and a large amount of unlabeled data. The goal is to leverage the unlabeled data to improve the performance of a model that would otherwise be trained on only the limited labeled examples.
In supervised learning, a model learns from data where each input is paired with a correct output.
Common techniques in semiovervåket learning include self-training, where a model trained on labeled data is used
Semiovervåket learning is particularly useful in scenarios where obtaining large amounts of labeled data is expensive