Data Source

Data source selection and utilization are critical for effective machine learning, particularly when dealing with limited or imbalanced datasets. Current research focuses on optimizing data integration strategies, including techniques like transfer learning (leveraging pre-trained models and selecting relevant source data subsets), retrieval-augmented generation (incorporating external knowledge bases), and synthetic data generation to augment existing datasets. These advancements aim to improve model performance, address data scarcity issues, and enhance the robustness and generalizability of machine learning models across diverse applications, impacting fields ranging from medical imaging to manufacturing.