基于傅立叶核函数支持向量机的性能与在函数回归中的应用研究
首发时间:2005-02-07
摘要:支持向量机(SVM)作为一种构建在统计学习理论上的新兴方法,在人工智能等领域与传统方法相比较有着更优良的性能,尤其在处理模式识别问题上,支持向量机有着广泛的应用。但在函数回归问题上,目前支持向量机的应用并不多,而且核函数的选择也普遍采用径向基核,对于特殊核的应用研究很少。本文针对信号处理中的回归问题,研究了基于傅立叶核函数支持向量机的性能,并分析了参数q对其性能的影响;根据推导得出傅立叶核函数在单个周期内积分为常数的结论,并提出了等效核宽度的概念;最后的仿真结果证实了,在信号处理领域,采用傅立叶核函数的支持向量机比采用径向基核函数的有着更好的性能。
For information in English, please click here
Research of the Performance of SVM Based on Fourier Kernel and Application on Data Regression
Abstract:As a new method built on Statistical Learning Theory, SVM has better performance in the field of AI contrast to traditional methods. Particularly, SVM has widely applications on pattern recognition, but fewer on regression now. And the common choice of kernel function is Radial Basis Function, so few study on other special kernels. In this paper, the performance of SVM based on Fourier kernel is studied which aims at the regression in signal processing questions, and the influence of parameter q on performance of SVM is analyzed. A conclusion is drew that the integral of Fourier kernel in one period is a constant and the concept of equivalent kernel function width is proposed. At last, simulation verifies that SVM based on Fourier kernel has better performance than the one based on RBF kernel in the field of signal processing.
Keywords: SVM, Fourier kernel, regression
论文图表:
引用
No.1546189321107738****
同行评议
共计0人参与
勘误表
基于傅立叶核函数支持向量机的性能与在函数回归中的应用研究
评论
全部评论0/1000