Epoch Based

Epoch-based methods, encompassing iterative training processes divided into distinct epochs, are central to various machine learning applications. Current research focuses on optimizing epoch selection strategies for improved model performance and robustness, particularly addressing challenges like overfitting and the "unknown dilemma" in verification. These advancements are impacting diverse fields, from federated learning in manufacturing to deep reinforcement learning for dynamic vehicle dispatching and biomedical signal processing, improving model accuracy and efficiency. The development of novel algorithms and refined techniques for managing epochs within these diverse contexts is a significant area of ongoing investigation.