pseudolabelingtekniikoilla
Pseudolabeling is a semi-supervised learning technique used to improve the performance of machine learning models by leveraging unlabeled data. The core idea is to first train a model on a labeled dataset, and then use this trained model to predict labels for a separate set of unlabeled data. These predicted labels, known as "pseudolabels," are then treated as if they were true labels.
Once the pseudolabels are generated, the original labeled data is combined with the unlabeled data that has
Various strategies exist for generating and using pseudolabels. A common approach involves setting a confidence threshold,