**6. Conclusions**

In this research, we propose MDSVC, which employs the mean and variance, leveraged by marginal theory and SVM. The novelty of MDSVC lies in its reconstruction of the hyperplane, reducing the number of support vector points compared to SVC under the same conditions, and the improvement in generalization performance. We also have theoretically proven that our generalization performance has been improved, and the error has an upper bound. To optimize the objective function of MDSVC, we employ the DCD method with high applicability and efficiency. Experimental results in most datasets show that MDSVC achieves better performance, which indicates its superiority.

In our future work, we will study the partition of the second stage to further improve the performance of our method. At the same time, to assess the application potential of our algorithm, we will employ our model in more application scenarios.

**Author Contributions:** Data curation, X.X. and J.C.; Formal analysis, Y.W. and J.C.; Methodology, Y.W. and J.C.; Software, J.C. and S.Y.; Resources, Y.W.; Supervision, L.H.; Writing—original draft, J.C.; Writing—review and editing, W.P., S.Z. (Shuangquan Zhang) and S.Z. (Shishun Zhao). All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China (No. 62072212), the Development Project of Jilin Province of China (Nos. 20200401083GX, 2020C003, 20200403172SF), and Guangdong Key Project for Applied Fundamental Research (No. 2018KZDXM076). This work was also supported by Jilin Province Key Laboratory of Big Data Intelligent Computing (No. 20180622002JC).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The implementation is publicly available at http://github.com/ Galichen/MDSVC (accessed on 3 November 2021).

**Conflicts of Interest:** The authors declare no conflict of interest.
