A new trigonometric kernel function for support vector machine

TL;DR

This paper introduces a novel one-parameter trigonometric kernel function for SVMs, which enhances classification accuracy compared to traditional kernels, demonstrated through empirical evaluations on various datasets.

Contribution

The paper proposes a new positive definite trigonometric kernel function for SVMs, expanding the kernel options and improving classification performance.

Findings

The new kernel outperforms Gaussian and polynomial kernels in accuracy.

Mixed kernels combining the new and Gaussian kernels yield the best results.

Empirical results include improved SVM and SVR performance on multiple datasets.

Abstract

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.