A set of new kernel function for support vector machines: An approach based on Chebyshev polynomials

机译：支持向量机的一组新内核函数：一种基于Chebyshev多项式的方法

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In this paper, we introduce a set of new kernel functions Which is derived by combining generalized Chebyshev polynomials with other standard kernel functions. New kernel functions have significant advantages over classic support Vector Machine's (SVM) kernel functions and Chebyshev kernel. Simulation results illustrate the fact that the new set of kernel functions (in particular Chebyshev-Gaussian kernel) has noticeable improvement in decreasing error rate and support vector numbers.

机译：在本文中，我们介绍了一组新的内核函数，这些函数是通过将广义Chebyshev多项式与其他标准内核函数相结合而得出的。与经典支持向量机（SVM）内核功能和Chebyshev内核相比，新内核功能具有显着优势。仿真结果说明了这样一个事实，即新的内核函数集（尤其是Chebyshev-Gaussian内核）在降低错误率和支持向量数方面具有显着的改进。

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《International Conference on Computer and Knowledge Engineering》|2013年|412-416|共5页
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Zafar Jafarzadeh Sara; Aminian Mohammad; Efati Sohrab;
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Linear and non-linear modeling; SVM; classification; mixing kernel; orthogonal polynomials;

机译：线性与非线性建模; SVM;分类;混合核;正交多项式;

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A set of new kernel function for support vector machines: An approach based on Chebyshev polynomials

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