核(代数)
变核密度估计
数学
核密度估计
估计员
多元核密度估计
多元统计
统计
分布的核嵌入
叠加原理
核方法
核回归
应用数学
数学分析
人工智能
计算机科学
组合数学
支持向量机
作者
Leo Breiman,William S. Meisel,Edward M. Purcell
出处
期刊:Technometrics
[Informa]
日期:1977-05-01
卷期号:19 (2): 135-144
被引量:347
标识
DOI:10.1080/00401706.1977.10489521
摘要
A class of density estimates using a superposition of kernels where the kernel parameter can depend on the nearest neighbor distances is studied by the use of simulated data. Their performance using several measures of error is superior to that of the usual Parzen estimators. A tentative solution is given to the problem of calibrating the kernel peakedness when faced with a finite sample set.
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