Finite-time-convergent support vector neural dynamics for classification

Support vector machine (SVM) is a popular binary classification algorithm widely utilized in various fields due to its accuracy and versatility. However, most of the existing research involving SVMs stays at the application level, and there is few research on optimizing the support vector solving process. Therefore, it is an alternative way to optimize the support vector solving process for improving the classification performance via constructing new solving methods. Recent research has demonstrated that neural dynamics exhibit robust solving performance and high accuracy. Motivated by this inspiration, this paper leverages neural dynamics to improve the accuracy and robustness of SVM solutions. Specifically, this paper models the solving process of SVM as a standard quadratic programming (QP) problem. Then, a support vector neural dynamics (SVND) model is specifically developed to provide the optimal solution to the aforementioned QP problem, with theoretical analysis confirming its ability to achieve global convergence. Datasets of varying sizes from various sources are employed to validate the effectiveness of the designed SVND model. Experimental results show that the designed SVND model demonstrates superior classification accuracy and robustness compared to other classical machine learning algorithms. The source code is available at https://github.com/LongJin-lab/NC_SVND.