Error Scaling

Error scaling research investigates how the error rate of machine learning models changes with increasing training data size. Current work focuses on understanding this relationship in diverse contexts, including discrete combinatorial spaces (like molecules and proteins), agnostic PAC learning settings, and applications like medical image segmentation and speech recognition, often employing kernel methods and support vector machines. These studies aim to improve model performance and fairness by identifying optimal data requirements and developing techniques to mitigate error, ultimately impacting the reliability and effectiveness of machine learning across various domains.