Constructing loss functions for optimal performance in differential machine learning

Abstract

In differential machine learning, the stochastic gradient flow method is often used to find the "nearminimum" point of the loss function, which corresponds to optimizing the classification algorithm. Despite the crucial role of the loss function in this process, its theoretical foundation has not been fully developed. This paper aims to contribute to the theoretical foundation of loss functions, providing a more detailed and systematic framework to support the development of more effective optimization and classification methods. We also present analyses on how the loss function impacts model performance and propose several improvements in the design and use of loss functions to achieve optimal performance. These studies not only help to better understand the nature of loss functions but also pave the way for new applications of differential machine learning in practical problems. Through this, the paper hopes to enhance the quality and efficiency of current machine learning models.