Semikontrollitud
Semikontrollitud refers to a type of machine learning where the training dataset contains a small amount of labeled data and a large amount of unlabeled data. This contrasts with supervised learning, which uses entirely labeled data, and unsupervised learning, which uses entirely unlabeled data. The goal of semikontrollitud learning is to leverage the abundant unlabeled data to improve the performance of a model that would otherwise be trained on limited labeled examples.
The underlying principle is that the unlabeled data, even without explicit labels, can still provide valuable
Common techniques in semikontrollitud learning include self-training, co-training, and graph-based methods. Self-training involves training a model