The Large Margin Distribution Machine (LDM) is a new approach in machine learning that focuses on optimizing the distribution of margins rather than just maximizing the minimum margin like traditional methods. By considering both the average margin size and its variability, LDM aims to improve generalization performance. This method can be applied in various areas where Support Vector Machine (SVM) is used, and it has been shown to outperform SVM both theoretically and in practical experiments.