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基于多尺度重采样思想的类指数核函数构造

胡站伟 焦立国 徐胜金 黄勇

胡站伟, 焦立国, 徐胜金, 黄勇. 基于多尺度重采样思想的类指数核函数构造[J]. 电子与信息学报, 2016, 38(7): 1689-1695. doi: 10.11999/JEIT151101
引用本文: 胡站伟, 焦立国, 徐胜金, 黄勇. 基于多尺度重采样思想的类指数核函数构造[J]. 电子与信息学报, 2016, 38(7): 1689-1695. doi: 10.11999/JEIT151101
HU Zhanwei, JIAO Liguo, XU Shengjin, HUANG Yong. Design of An Exponential-like Kernel Function Based on Multi-scale Resampling[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1689-1695. doi: 10.11999/JEIT151101
Citation: HU Zhanwei, JIAO Liguo, XU Shengjin, HUANG Yong. Design of An Exponential-like Kernel Function Based on Multi-scale Resampling[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1689-1695. doi: 10.11999/JEIT151101

基于多尺度重采样思想的类指数核函数构造

doi: 10.11999/JEIT151101
基金项目: 

国家自然科学基金(11472158)

Design of An Exponential-like Kernel Function Based on Multi-scale Resampling

Funds: 

The National Natural Science Foundation of China (11472158)

  • 摘要: 该文按照多尺度重采样思想,构造了一种类指数分布的核函数(ELK),并在核回归分析和支持向量机分类中进行了应用,发现ELK对局部特征具有捕捉优势。ELK分布仅由分析尺度决定,是单参数核函数。利用ELK对阶跃信号和多普勒信号进行Nadaraya-Watson回归分析,结果显示ELK降噪和阶跃捕捉效果均优于常规Gauss核,整体效果接近或优于局部加权回归散点平滑法(LOWESS)。多个UCI数据集的SVM分析显示,ELK与径向基函数(RBF)分类效果相当,但比RBF具有更强的局域性,因此具有更细致的分类超平面,同时分类不理想时可能产生更多的支持向量。对比而言,ELK对调节参数敏感性低,这一性质有助于减少参数优选的计算量。单参数的ELK对局域特征的良好捕捉能力,有助于这类核函数在相关领域得到推广。
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出版历程
  • 收稿日期:  2015-09-25
  • 修回日期:  2016-05-03
  • 刊出日期:  2016-07-19

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