电邮这篇文章 Analysis of Optimization Methods for Nonparametric Estimation of the Probability Density with Respect to the Blur Factor of Kernel Functions
- 作者: Lapko A.V.1,2, Lapko V.A.1,2
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隶属关系:
- Institute of Computational Modeling, Siberian Branch, Russian Academy of Sciences
- Reshetnev Siberian State University of Science and Technology
- 期: 卷 60, 编号 6 (2017)
- 页面: 515-522
- 栏目: General Problems of Metrology and Measurement Technique
- URL: https://journals.rcsi.science/0543-1972/article/view/246181
- DOI: https://doi.org/10.1007/s11018-017-1228-x
- ID: 246181
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详细
The results of a comparison of the most common optimization methods for the nonparametric estimation of the probability density of Rosenblatt–Parzen are presented. To select the optimal values of the blur coefficients of kernel functions, minimum conditions for the standard deviation of the nonparametric estimate of the probability density and the maximum of the likelihood function are used.
作者简介
A. Lapko
Institute of Computational Modeling, Siberian Branch, Russian Academy of Sciences; Reshetnev Siberian State University of Science and Technology
编辑信件的主要联系方式.
Email: lapko@icm.krasn.ru
俄罗斯联邦, Krasnoyarsk; Krasnoyarsk
V. Lapko
Institute of Computational Modeling, Siberian Branch, Russian Academy of Sciences; Reshetnev Siberian State University of Science and Technology
Email: lapko@icm.krasn.ru
俄罗斯联邦, Krasnoyarsk; Krasnoyarsk
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