论文标题
支持向量机的新三角内核功能
A new trigonometric kernel function for support vector machine
论文作者
论文摘要
在过去的几年中，已经引入了各种类型的机器学习算法，例如支持向量机（SVM），支持向量回归（SVR）和非负矩阵分解（NMF）。内核方法是提高机器学习算法的分类精度的有效方法。本文介绍了一个单参数内核功能，以提高SVM分类的准确性。所提出的内核函数由三角项组成，与所有现有内核函数不同。我们显示此函数是一个积极的确定内核函数。最后，我们根据新的三角核，高斯内核，多项式内核以及新内核函数和高斯内核功能在各种类型的数据集上评估SVM方法。经验结果表明，基于新的三角内核函数和混合核函数的SVM达到了最佳的分类精度。此外，提出了基于新的三角核函数和混合内核函数执行SVR的一些数值结果。
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.
