Мақаланы E-mail арқылы жіберу Analysis of Optimization Methods for Nonparametric Estimation of the Probability Density with Respect to the Blur Factor of Kernel Functions
- Авторлар: Lapko A.V.1,2, Lapko V.A.1,2
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Мекемелер:
- Institute of Computational Modeling, Siberian Branch, Russian Academy of Sciences
- Reshetnev Siberian State University of Science and Technology
- Шығарылым: Том 60, № 6 (2017)
- Беттер: 515-522
- Бөлім: 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
Дәйексөз келтіру
Ашық рұқсат
Рұқсат берілді
Тек жазылушылар үшін
Аннотация
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.
Авторлар туралы
A. Lapko
Institute of Computational Modeling, Siberian Branch, Russian Academy of Sciences; Reshetnev Siberian State University of Science and Technology
Хат алмасуға жауапты Автор.
Email: lapko@icm.krasn.ru
Ресей, 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
Ресей, Krasnoyarsk; Krasnoyarsk
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