A new trigonometric kernel function for support vector machine

作     者：Fathi Hafshejani, Sajad Moaberfard, Zahra 

作者机构：Department of Applied Mathematics Shiraz University of Technology Modarres Boulevard Fars Shiraz 71557-13876 Iran Department of Math and Computer Science University of Lethbridge 4401 University Dr W Lethbridge T1K 3M4 AB Canada 

出 版 物：《Iran Journal of Computer Science》 (Iran J Comput Sci)

年 卷 期：2023年第6卷第2期

页      面：137-145页

主　　题：Kernel method Support vector machine Trigonometric kernel function 

摘      要：In the last few years, various types of machine learning algorithms, such as support vector machine (SVM), support vector regression (SVR), and non-negative matrix factorization (NMF) have been introduced. The kernel approach is an effective method for increasing the classification accuracy of machine learning algorithms. This paper introduces a family of one-parameter kernel functions for improving the accuracy of SVM classification. The proposed kernel function consists of a trigonometric term and differs from all existing kernel functions. We show this function is a positive definite kernel function. Finally, we evaluate the SVM method based on the new trigonometric kernel, the Gaussian kernel, the polynomial kernel, and a convex combination of the new kernel function and the Gaussian kernel function on various types of datasets. Empirical results show that the SVM based on the new trigonometric kernel function and the mixed kernel function achieve the best classification accuracy. Moreover, some numerical results of performing the SVR based on the new trigonometric kernel function and the mixed kernel function are presented. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022.