Semi-supervised learning is a combination of supervised and unsupervised learning. It involves training an algorithm using a dataset that is partially labeled. The algorithm learns from both the labeled and unlabeled data to improve its performance. Semi-supervised learning can be helpful when obtaining labeled data is expensive or time-consuming.
Updated 5 months ago