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The Unified Chebyshev polynomial kernel function for support vector regression machine

机译:支持向量回归机器的统一Chebyshev多项式内核功能

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Support vector regression machine (SVR) has become a promising tool in many research fields, such as web intelligence, machinery fault diagnostic technique, dynamics environmental forecasting, and earthquake prediction, etc. Kernel method is most important to get more robust and higher generalization ability of SVR. In this paper, a new kernel, named Unified Chebyshev polynomial kernel (UCK), is proposed for SVR. Firstly, a group of new Unified Chebyshev polynomials are constructed using Chebyshev polynomials. Therefore, on the basis of these polynomials, a Unified Chebyshev polynomials kernel is proposed and has been proved satisfying Mercer condition. The simulation results show that UCK can lead to better generalization performance in comparison with other common kernels on many benchmark data sets.
机译:支持向量回归机(SVR)已成为许多研究领域的有希望的工具,如网络智能,机械故障诊断技术,动态环境预测和地震预测等。内核方法最重要的是获得更强大和更高的概括能力 SVR。 本文提出了一个名为Unified Chebyshev多项式内核(UCK)的新内核,用于SVR。 首先,使用Chebyshev多项式构建一组新的统一Chebyshev多项式。 因此,在这些多项式的基础上,提出了一个统一的Chebyshev多项式内核,并已证明符合Mercer条件。 仿真结果表明,与许多基准数据集上的其他常见内核相比,UCK可以导致更好的泛化性能。

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