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.

翻译：在过去几年中,采用了各种类型的机器学习算法,如支持矢量机(SVM)、支持矢量递退(SVR)和非负式矩阵因子化(NMF)等。内核法是提高机器学习算法分类准确性的有效方法。本文引入了一组单数内核功能,以提高SVM分类的准确性。提议的内核函数由三角词组成,与所有现有的内核函数不同。我们显示了这一函数是肯定的内核函数。最后,我们根据新的三角内核、高斯内核、多核内核以及新内核函数和各类数据集高斯内核内核函数的内核组合,对SVM方法进行了评估,根据新的三角内核内核函数和混合内核函数得出了最佳的精确性。此外,根据新的三角内核函数和混合内核的混合内核函数是一些数字性结果。