Semi-supervised trees for multi-target regression

摘要

The predictive performance of traditional supervised methods heavily depends on the amount of labeled data. However, obtaining labels is a difficult process in many real-life tasks, and only a small amount of labeled data is typically available for model learning. As an answer to this problem, the concept of semi-supervised learning has emerged. Semi supervised methods use unlabeled data in addition to labeled data to improve the performance of supervised methods.