Learning Theory

Learning theory is the theoretical foundation of machine learning algorithms, aiming to understand the essence, performance limits, and optimization methods of the learning process. It explores the relationships between data, model complexity, and generalization ability through mathematical models and statistical analysis, providing theoretical guidance for designing efficient and reliable machine learning systems. The research findings in this field are of significant practical value for improving the accuracy and robustness of models.