a new insight on the model of support vector machine

Support vector machine (svm) is a powerful classification algorithm that separates samples by finding an optimal decision boundary. its performance can degrade when feature variances differ across classes, potentially leading to suboptimal decision boundaries. a variance-weighted framework is proposed that reduces the influence of high-variance features while enhancing the impact of low-variance features, resulting in more accurate and robust decision boundaries. the method is applicable in both linear and nonlinear settings. evaluation on synthetic datasets and real-world datasets, including breast cancer and a9a, using cross-validation demonstrates that the variance-weighted svm achieves higher accuracy and f1-score compared to soft svm and ldm, particularly in scenarios with significant variance differences between classes.