Transductive Approach

Transductive learning leverages unlabeled data during model training to improve prediction accuracy and efficiency, contrasting with inductive methods that train solely on labeled data. Current research focuses on applying transductive approaches to various machine learning tasks, including link prediction, few-shot learning, and novelty detection, often employing techniques like graph neural networks, generative adversarial networks, and adaptive scoring methods. This approach is particularly valuable in scenarios with limited labeled data or where exploiting the structure of unlabeled data can significantly enhance model performance, impacting fields like computer vision, natural language processing, and reinforcement learning.