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基于多尺度重采樣思想的類指數(shù)核函數(shù)構(gòu)造

胡站偉 焦立國 徐勝金 黃勇

胡站偉, 焦立國, 徐勝金, 黃勇. 基于多尺度重采樣思想的類指數(shù)核函數(shù)構(gòu)造[J]. 電子與信息學報, 2016, 38(7): 1689-1695. doi: 10.11999/JEIT151101
引用本文: 胡站偉, 焦立國, 徐勝金, 黃勇. 基于多尺度重采樣思想的類指數(shù)核函數(shù)構(gòu)造[J]. 電子與信息學報, 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

基于多尺度重采樣思想的類指數(shù)核函數(shù)構(gòu)造

doi: 10.11999/JEIT151101
基金項目: 

國家自然科學基金(11472158)

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

Funds: 

The National Natural Science Foundation of China (11472158)

  • 摘要: 該文按照多尺度重采樣思想,構(gòu)造了一種類指數(shù)分布的核函數(shù)(ELK),并在核回歸分析和支持向量機分類中進行了應用,發(fā)現(xiàn)ELK對局部特征具有捕捉優(yōu)勢。ELK分布僅由分析尺度決定,是單參數(shù)核函數(shù)。利用ELK對階躍信號和多普勒信號進行Nadaraya-Watson回歸分析,結(jié)果顯示ELK降噪和階躍捕捉效果均優(yōu)于常規(guī)Gauss核,整體效果接近或優(yōu)于局部加權(quán)回歸散點平滑法(LOWESS)。多個UCI數(shù)據(jù)集的SVM分析顯示,ELK與徑向基函數(shù)(RBF)分類效果相當,但比RBF具有更強的局域性,因此具有更細致的分類超平面,同時分類不理想時可能產(chǎn)生更多的支持向量。對比而言,ELK對調(diào)節(jié)參數(shù)敏感性低,這一性質(zhì)有助于減少參數(shù)優(yōu)選的計算量。單參數(shù)的ELK對局域特征的良好捕捉能力,有助于這類核函數(shù)在相關(guān)領(lǐng)域得到推廣。
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出版歷程
  • 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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